Systems and methods for noise reduction in imaging

ABSTRACT

Systems and methods are provided for the denoising of images in the presence of broadband noise based on the detection and/or estimation of in-band noise. According to various example embodiments, an estimate of broadband noise that lies within the imaging band is made by detecting or characterizing the out-of-band noise that lies outside of the imaging band. This estimated in-band noise may be employed for denoise the detected imaging waveform. According to other example embodiments, a reference receive circuit that is sensitive to noise within the imaging band, but is isolated from the imaging energy, may be employed to detect and/or characterize the noise within the imaging band. The estimated reference noise may be employed to denoise the detected in-band imaging waveform.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.62/463,431, titled “SYSTEMS AND METHODS FOR NOISE REDUCTION IN IMAGING”and filed on Feb. 24, 2017, the entire contents of which is incorporatedherein by reference.

BACKGROUND

The present disclosure relates to imaging technologies and theprocessing of imaging data for the removal of noise.

Medical imaging with ultrasound and MRI imaging relies on detecting lowamplitude signals in the radiofrequency spectrum, typically spanningfrom 2 MHz to 200 MHz. Image quality is greatly influenced by thesignal-to-noise ratio. In intravascular ultrasound (IVUS), intracardiacechocardiography (ICE) and other forms of minimally invasive ultrasound,the ultrasound transducer detects ultrasound signals from thesurrounding structures and converts the acoustic energy into anelectrical signal. This signal is then transmitted along one or moreconductive channels (such as coaxial conductors, twisted pairconductors, flex circuits etc.). For many reasons, (including cost,manufacturability, safety, biocompatibility, thermal concerns, andrequirements for provision of power) the portion of the minimallyinvasive imaging probe that can be inserted intracorporeally often doesnot contain an amplifier to boost the signal strength. The electricalsignals detected by minimally invasive ultrasound transducers can bevery small (<10 mV and more typically <1 mv), and much of theinformation about tissue structures that can be imaged with ultrasoundtends to lie in the lower portion of the dynamic range of the electricalsignals that are detected. The signal amplitude of a received ultrasoundsignal is limited by any or all of the mechanical efficiency of thetransducer, the low amplitude of the acoustic signals detected, thesmall size of the transducer and attenuation along the conductors thatcarry the electrical signal from the transducer out of the body. Inlight of this, the signals in minimally invasive ultrasound imagingsystems tend to be very weak.

Noise can be introduced into the system from many sources, includingradio transmitters, power electronics, transmission lines, switchingtransistors and others known in the art. Noise can be introduced viainduction or directly via conduction and suboptimal isolation ofcomponents that are sensitive to electromagnetic interference. Some ofthe noise may be generated by components within the imaging systemitself, such as scanning actuators, pulse width modulators for motorcontrollers, switched mode power supplies, clocking circuits andtransistors in any of the electronic components of an imaging system.Furthermore, other systems coupled to a patient or in the proceduralenvironment, such as impedance monitors, tracking systems (like thosefound in Carto® 3, Carto® XP or NavX™ systems), temperature sensors,infusion pumps, ablation systems, ECG and hemodynamic monitors canintroduce noise. RFID inventory control systems used in some clinicalareas can also introduce noise.

Several approaches are directed at reducing the amount of noise thatenters into the ultrasound receive circuitry of ultrasound imagingsystems, including selection of components within the system thatgenerate minimal RF noise, electrical isolation, shielding, propergrounding, and physically separating noise-generating components fromcomponents that are susceptible to electromagnetic noise. Theseapproaches are often difficult to implement, as the sources of the noiseoften have preferred characteristics for other reasons (i.e. pulse widthmodulated motor controllers are energy efficient and have good responsetimes) or are difficult to physically isolate from one another (i.e. itmay be desirable to have power electronics in close proximity to theimaging probe or its associated circuitry).

Other approaches for reducing the effect of noise on ultrasound signalquality (and hence ultrasound image quality) include filtering and imageprocessing. Ultrasound signals typically have a known bandwidth and thedetected ultrasound signal may be filtered using either analog ordigital filtering techniques (often a combination of the two). Analog ordigital filtering can be applied to limit the portions of the electricalsignal output from the ultrasound receive circuitry to those portionswhose frequencies lie within the operational bandwidth (or harmonicsthereof) of the ultrasound transducer. Selecting filters with narrowbandwidths and sharp cutoffs can reduce the amount of noise that isallowed into the signals used to generate images or otherwise make useof the ultrasound signals (such as for Doppler measurements spectralanalysis of the ultrasound signal, or assessment of flow of scatterersin the sonicated field). Notch or comb filters are helpful in removingnarrowband noise within the imaging range of frequencies. Overlyaggressive filtering can have the unwanted effect of reducing the amountof signal power that gets accepted for generating images or for otheruse of the ultrasound signals. It may also negatively impact otherperformance aspects of an ultrasound imaging system, such as resolution.However, if the passband of the filters is too large, then more noise isaccepted into the system.

Image processing can further reduce the noise by filtering the imagedata generated, such as by averaging or removing outlier values. Forexample, such filtering can be applied within the image in the spatialdomain by applying a Gaussian filter to a pixel and its neighboringpixels in order to blur or smoothen out any random noise in the image.Unfortunately, this tends to reduce the spatial resolution of the image.Similarly, spatial domain filtering can be applied in the structuresbeing imaged that do not move rapidly with respect to the framerepetition frequency of the imaging modality. For example, a pixel in animage frame can be the average or Gaussian-filtered result of the pixelsat similar positions in one or more preceding and/or trailing frames.

Similar problems apply to MRI imaging systems, where weak signals aredetected in the presence of noise from undesired sources ofradiofrequency energy.

What would be very helpful are methods, systems and devices to identifynoise and actively remove the noise from one or more imaging signals.

Many forms of noise enter into the ultrasound receive signal chain andcan become difficult to remove once they enter the system, especially ifthey are broadband in nature, wherein a portion of the noise lies withinthe passband of the ultrasound system. For example, in an imaging systemthat has a transducer with a center frequency of 10 MHz, and a passbandof 7.5 to 12.5 MHz, the system may be designed to heavily filter out anyportions of the noise that are less than 7.5 MHz and any portions of thenoise that are more than 12.5 MHz. Unfortunately, the amplitude of thenoise within the 7.5-12.5 MHz bandpass may frequently be appreciablerelative to the amplitude of the ultrasound signal that is beingdetected.

Many sources of noise occur as a result of rapid transients, such aswhen a field effect transistor or switch turns on or off. An electricalsignal with rapid transients in it has a very broad frequency domainrepresentation that can easily span all or a portion of the passband ofthe ultrasound receive signal chain. This is particularly true of powersupplies or pulse width modulation circuits where the noise can have astrong enough amplitude to compete with the signal being detected.

SUMMARY

One approach to reduce broadband noise exploits the fact that imagingenergy predominantly lies within a selective imaging band but thatbroadband noise can be detected both within the imaging band and outsideof the imaging band. In principle, by detecting or characterizing noiseoutside of the imaging band at any point in time, one can estimate thebroadband noise that might lie within the imaging band and alter thedetected signal to reduce the estimated in-band noise. By effectivelycreating an estimate of the in-band noise based on out-of-band noise,one can generate a signal that estimates the desired imaging energy inthe absence of the estimated in-band noise.

Another approach to reduce noise within the imaging band is to usereference receive circuits (comprising resistors, capacitors, inductors,transmission lines, amplifiers, transformers, inactivated transducers orcomponents that can emulate a transducer receive circuit) that aresensitive to noise in the imaging band, but are isolated from theimaging energy. By estimating the in-band imaging noise based on in-bandnoise received by the reference receive circuit, one can generatesignals that estimate the desired imaging energy in the absence of theestimated in-band noise.

In one aspect, there is provided a method of denoising imaging signalsdetected in the presence of broadband noise, the method comprising:

in the absence of receiving imaging energy, detecting energy waves withan imaging transducer receive circuit, thereby obtaining a noisecharacterization waveform, and filtering the noise characterizationwaveform to generate an in-band noise characterization waveform residingwithin an imaging band and an out-of-band noise characterizationwaveform residing within a noise-detection band that lies, at least inpart, beyond the imaging band;

segmenting the in-band noise characterization waveform and theout-of-band noise characterization waveform according to one or moretime windows;

for at least one time window, processing the in-band noisecharacterization waveform and the out-of-band noise characterizationwaveform to determine a relationship between noise in the imaging bandand noise in the noise-detection band;

detecting imaging signals with the imaging transducer receive circuitthereby obtaining one or more imaging waveforms;

for at least one imaging waveform:

-   -   a) filtering the imaging waveform to generate an in-band imaging        waveform residing within the imaging band and an out-of-band        noise-detection imaging waveform residing within the        noise-detection band;    -   b) segmenting the in-band imaging waveform and the out-of-band        noise-detection imaging waveform according to one or more time        windows;    -   c) employing the relationship and the out-of-band        noise-detection imaging waveform to estimate, within at least        one time window, a measure associated with the amount of noise        in the in-band imaging waveform; and    -   d) for at least one time window processed in c), applying a        denoising correction to the portion of the in-band imaging        waveform within the time window.

In another aspect, there is provided a method of denoising imagingsignals detected in the presence of noise, the method comprising:

in the absence of receiving imaging energy:

-   -   detecting energy waves with an imaging transducer receive        circuit, thereby obtaining a noise characterization waveform,        and filtering the noise characterization waveform to generate an        in-band noise characterization waveform residing within an        imaging band; and    -   detecting noise with a reference receive circuit configured to        avoid transduction of imaging energy while detecting noise        received by the imaging transducer receive circuit, thereby        obtaining a reference noise characterization waveform;

processing the in-band noise characterization waveform and the referencenoise characterization waveform to determine a relationship betweennoise in the imaging band and noise detected by the reference receivecircuit;

detecting imaging signals with the imaging transducer receive circuit,thereby obtaining one or more imaging waveforms;

for at least one imaging waveform:

-   -   a) filtering the imaging waveform to generate an in-band imaging        waveform residing within the imaging band;    -   b) detecting, with the reference receive circuit, a reference        noise-detection waveform;    -   c) segmenting the in-band imaging waveform and the reference        noise-detection waveform according to one or more time windows;    -   d) employing the relationship and the reference noise-detection        waveform to estimate, within at least one time window, a measure        associated with the amount of noise in the in-band imaging        waveform; and    -   e) for at least one time window processed in c), applying a        denoising correction to the portion of the in-band imaging        waveform within the time window.

In another aspect, there is provided a method of denoising imagingsignals detected in the presence of broadband noise, the methodcomprising:

-   -   detecting energy waves with an imaging transducer receive        circuit, thereby obtaining an imaging waveform, and filtering        the imaging waveform to generate an in-band imaging waveform        residing within an imaging band and an out-of-band        noise-detection imaging waveform residing within a        noise-detection band that lies, at least in part, beyond the        imaging band;    -   detecting an in-band imaging envelope of the in-band imaging        waveform;    -   detecting an out-of-band envelope of the out-of-band        noise-detection imaging waveform;    -   applying a scaling factor to the out-of-band envelope, thereby        obtaining a modified out-of-band envelope; and    -   combining the modified out-of-band envelope and the in-band        imaging envelope to obtain a noise-corrected in-band envelope;    -   wherein the scaling factor is selected to reduce a contribution        of in-band noise in the noise-corrected in-band envelope.

In another aspect, there is provided a method of denoising imagingsignals detected in the presence of noise, the method comprising:

-   -   detecting energy waves with an imaging transducer receive        circuit, thereby obtaining an imaging waveform, and filtering        the imaging waveform to generate an in-band imaging waveform        residing within an imaging band and an out-of-band        noise-detection imaging waveform residing within a        noise-detection band that lies, at least in part, beyond the        imaging band;    -   applying a frequency shift and an amplitude scaling factor to        the out-of-band noise-detection imaging waveform, thereby        obtaining a modified waveform, such that the modified waveform        includes frequency components residing within the imaging band;        and    -   combining the modified waveform and the in-band imaging waveform        to obtain a noise-corrected in-band imaging waveform;    -   wherein the amplitude scaling factor is selected to reduce a        contribution of in-band noise in the noise-corrected in-band        imaging waveform.

In another aspect, there is provided a method of denoising imagingsignals detected in the presence of noise, the method comprising:

-   -   detecting energy waves with an imaging transducer receive        circuit, thereby obtaining an imaging waveform, and filtering        the imaging waveform to generate an in-band imaging waveform        residing within an imaging band; and    -   detecting noise with a reference receive circuit configured to        avoid transduction of imaging energy while detecting noise        received by the imaging transducer receive circuit, thereby        obtaining a reference noise-detection waveform;    -   detecting an in-band imaging envelope of the in-band imaging        waveform;    -   detecting a reference envelope of the reference noise-detection        waveform;    -   applying a scaling factor to the reference envelope, thereby        obtaining a modified reference envelope; and    -   combining the modified reference envelope and the in-band        imaging envelope to obtain a noise-corrected in-band envelope;    -   wherein the scaling factor is selected to reduce a contribution        of in-band noise in the noise-corrected in-band envelope.

In another aspect, there is provided a method of denoising imagingsignals detected in the presence of noise, the method comprising:

-   -   detecting energy waves with an imaging transducer receive        circuit, thereby obtaining an imaging waveform, and filtering        the imaging waveform to generate an in-band imaging waveform        residing within an imaging band; and    -   detecting noise with a reference receive circuit configured to        avoid transduction of imaging energy while detecting noise        received by the imaging transducer receive circuit, thereby        obtaining a reference noise-detection waveform;    -   adaptively filtering the reference noise-detection waveform        according to one or more adaptive filter parameters; and    -   combining the filtered reference noise-detection waveform and        the in-band imaging waveform to obtain a noise-corrected in-band        imaging waveform;    -   wherein the adaptive filter parameters are actively determined        by processing the noise-corrected in-band imaging waveform to        minimize the power of the noise-corrected in-band imaging        waveform.

In another aspect, there is provided a method of denoising imagingsignals detected in the presence of broadband noise, the methodcomprising:

-   -   detecting energy waves with an imaging transducer receive        circuit, thereby obtaining an imaging waveform, and filtering        the imaging waveform to generate an in-band imaging waveform        residing within an imaging band and an out-of-band        noise-detection imaging waveform residing within a        noise-detection band that lies, at least in part, beyond the        imaging band;    -   detecting an in-band imaging envelope of the in-band imaging        waveform;    -   detecting an out-of-band imaging envelope of the out-of-band        noise-detection imaging waveform;    -   adaptively filtering the out-of-band imaging envelope according        to one or more adaptive filter parameters; and    -   combining the filtered out-of-band imaging envelope and the        in-band imaging envelope to obtain a noise-corrected in-band        imaging envelope;    -   wherein the adaptive filter parameters are actively determined        by processing the noise-corrected in-band imaging envelope to        minimize the power of the noise-corrected in-band imaging        envelope.

In another aspect, there is provided a method of denoising imagingsignals detected in the presence of broadband noise, the methodcomprising:

-   -   detecting energy waves with an imaging transducer receive        circuit, thereby obtaining an imaging waveform, and filtering        the imaging waveform to generate an in-band imaging waveform        residing within an imaging band and an out-of-band        noise-detection imaging waveform residing within a        noise-detection band that lies, at least in part, beyond the        imaging band;    -   applying a frequency shift to the out-of-band noise-detection        imaging waveform, thereby obtaining a modified waveform, such        that the modified waveform includes frequency components        residing within the imaging band;    -   adaptively filtering the out-of-band noise-detection imaging        waveform according to one or more adaptive filter parameters;        and    -   combining the filtered modified waveform and the in-band imaging        waveform to obtain a noise-corrected in-band imaging waveform;    -   wherein the adaptive filter parameters are actively determined        by processing the noise-corrected in-band imaging waveform to        minimize the power of the noise-corrected in-band imaging        waveform.

In another aspect, there is provided a method of denoising imagingsignals detected in the presence of broadband noise, the methodcomprising:

-   -   detecting energy waves with an imaging transducer receive        circuit, thereby obtaining an imaging waveform, and filtering        the imaging waveform to generate an in-band imaging waveform        residing within an imaging band and an out-of-band        noise-detection imaging waveform residing within a        noise-detection band that lies, at least in part, beyond the        imaging band;    -   processing the out-of-band noise-detection imaging waveform to        select suitable filter parameters of a dynamic digital filter        for filtering the in-band imaging waveform to remove in-band        noise; and filtering the in-band imaging waveform with the        dynamic digital filter according to the filter parameters.

In another aspect, there is provided a method of performing noisereduction on signals obtained by a detection system characterized by oneor more noise sources, comprising:

-   -   in the absence of receiving imaging energy, detecting energy        waves with an imaging transducer receive circuit, thereby        obtaining a noise characterization waveform, and filtering the        noise characterization waveform to generate an in-band noise        characterization waveform residing within an imaging band and an        out-of-band noise characterization waveform residing within a        noise-detection band that lies, at least in part, beyond the        imaging band;    -   segmenting the in-band noise characterization waveform and the        out-of-band noise characterization waveform according to one or        more time windows;    -   for at least one time window, processing the in-band noise        characterization waveform and the out-of-band noise        characterization waveform according to a pattern recognition        algorithm to identify a noise pattern within the noise-detection        band that is correlated with noise in the imaging band;    -   detecting imaging signals with the imaging transducer receive        circuit, thereby obtaining an imaging waveform and filtering the        imaging waveform to obtain an in-band imaging waveform residing        within the imaging band and an out-of-band noise-detection        imaging waveform residing within the noise-detection band;    -   segmenting the in-band imaging waveform and the out-of-band        imaging waveform according to one or more time windows;    -   for at least one time window, processing the out-of-band        noise-detection imaging waveform according to the pattern        recognition algorithm to detect of the noise pattern; and    -   in the event of detection of the noise pattern, applying a        denoising correction to the time window of the in-band imaging        waveform that is specific to the noise pattern detected in the        out-of-band noise-detection imaging waveform.

In another aspect, there is provided a method of performing noisereduction on signals obtained by a detection system characterized by oneor more known noise sources, comprising:

-   -   in the absence of receiving imaging energy:        -   detecting energy waves with an imaging transducer, thereby            obtaining a noise characterization waveform, and filtering            the noise characterization waveform to generate an in-band            noise characterization waveform residing within an imaging            band; and        -   detecting noise with a reference receive circuit configured            to avoid transduction of imaging energy while detecting            noise received by the imaging transducer receive circuit,            thereby obtaining a reference noise characterization            waveform;        -   segmenting the in-band noise characterization waveform and            the reference noise characterization waveform according to            one or more time windows;        -   for at least one time window, processing the in-band noise            characterization waveform and the reference noise            characterization waveform to determine a relationship            between noise in the imaging band and noise detected by the            reference receive circuit;    -   processing the in-band noise characterization waveform and the        reference noise characterization waveform according to a pattern        recognition algorithm to identify the presence of a noise        pattern within the reference noise characterization waveform        that is correlated with noise in the in-band noise        characterization waveform;    -   detecting imaging signals with the imaging transducer receive        circuit to obtain an imaging waveform, while also detecting a        reference noise-detection waveform with the reference receive        circuit, and filtering the imaging waveform to obtain an in-band        imaging waveform residing within the imaging band;    -   segmenting the in-band imaging waveform and the reference        noise-detection waveform according to one or more time windows;    -   for at least one time window, processing the reference        noise-detection waveform according to the pattern recognition        algorithm to detect the presence of the noise pattern; and    -   in the event of detection of the noise pattern, applying a        denoising correction to the time window of the in-band imaging        waveform that is specific to the noise pattern detected in the        reference noise-detection waveform.

In another aspect, there is provided a method of denoising imagingsignals detected in the presence of noise, the method comprising:

-   -   in the absence of receiving imaging energy, detecting energy        waves with an imaging transducer receive circuit, thereby        obtaining a noise characterization waveform, and filtering the        noise characterization waveform to generate an in-band noise        characterization waveform residing within an imaging band and an        out-of-band noise characterization waveform residing within a        noise-detection band that lies, at least in part, beyond the        imaging band;    -   detecting imaging signals with the imaging transducer receive        circuit, thereby obtaining an imaging waveform, and filtering        the imaging waveform to generate an in-band imaging waveform        residing within the imaging band and an out-of-band        noise-detection imaging waveform residing within the        noise-detection band;    -   performing a cross-correlation between the out-of-band imaging        waveform and the out-of-band noise characterization waveform to        determine a time delay associated with a maximum        cross-correlation; and    -   applying the time delay and an amplitude adjustment to the        in-band noise characterization waveform, thereby obtaining a        modified in-band noise characterization waveform, and        subtracting the modified in-band noise characterization waveform        from the in-band imaging waveform.

In another aspect, there is provided a method of denoising imagingsignals detected in the presence of noise, the method comprising:

-   -   in the absence of receiving imaging energy:        -   detecting energy waves with an imaging transducer receive            circuit, thereby obtaining a noise characterization            waveform, and filtering the noise characterization waveform            to generate an in-band noise characterization waveform            residing within an imaging band; and        -   detecting noise with a reference receive circuit configured            to avoid transduction of imaging energy while detecting            noise received by the imaging transducer receive circuit,            thereby obtaining a reference noise characterization            waveform;    -   detecting imaging signals with the imaging transducer receive        circuit to obtain an imaging waveform, while also detecting a        reference noise-detection waveform with the reference receive        circuit, and filtering the imaging waveform to obtain an in-band        imaging waveform residing within the imaging band;    -   performing a cross-correlation between the reference        noise-detection waveform and the reference noise        characterization waveform to determine a time delay associated        with a maximum cross-correlation; and    -   applying the time delay and an amplitude adjustment to the        in-band noise characterization waveform, thereby obtaining a        modified in-band noise characterization waveform, and        subtracting the modified in-band noise characterization waveform        from the in-band imaging waveform.

In another aspect, there is provided a method of denoising imagingsignals detected in the presence of noise, the method comprising:

-   -   detecting imaging signals with an imaging transducer receive        circuit along a plurality of adjacent scan lines, thereby        obtaining a plurality of imaging waveforms;    -   for at least two adjacent scan lines:        -   filtering the imaging waveform respectively associated            therewith to generate an in-band imaging waveform residing            within an imaging band and an out-of-band noise-detection            imaging waveform residing within a noise-detection band that            lies, at least in part, beyond the imaging band;        -   segmenting the in-band imaging waveform and the out-of-band            noise-detection imaging waveform according to a series of            time windows;        -   for at least one window:            -   processing the out-of-band noise-detection imaging                waveform to determine whether or not a corresponding                windowed portion of the in-band imaging waveform should                be noise corrected; and            -   in the event that the in-band imaging waveform within                the time window is deemed to be suitable for noise                correction, applying a denoising correction to the                in-band imaging waveform within the time window, wherein                the denoising correction for each sample in the window                is based on one or more statistical measures associated                with samples in the in-band imaging waveforms from two                or more adjacent windows, each adjacent window residing                in a respective adjacent scan line; and    -   generating an image based on denoised in-band imaging waveforms        respectively associated with the plurality of scan lines.

In another aspect, there is provided a method of denoising imagingsignals detected in the presence of noise, the method comprising:

-   -   detecting energy waves with an imaging transducer receive        circuit, thereby obtaining an imaging waveform, and filtering        the imaging waveform to generate an in-band imaging waveform        residing within an imaging band and an out-of-band        noise-detection imaging waveform residing within a        noise-detection band that lies, at least in part, beyond the        imaging band;    -   detecting imaging signals with an imaging transducer receive        circuit along a plurality of adjacent scan lines, thereby        obtaining a plurality of imaging waveforms;    -   processing one or more out-of-band noise-detection imaging        waveforms to determine the periodicity of a noise source,    -   adjusting the scan rate such that the noise is not temporally        synchronized in in-band imaging waveforms associated with        adjacent scan lines.    -   for at least two adjacent scan lines:        -   segmenting the in-band imaging waveform according to a            series of time windows;        -   for at least one window:            -   applying a denoising correction to the in-band imaging                waveform within the time window, wherein the denoising                correction for each sample in the window is based on one                or more statistical measures associated with samples in                the in-band imaging waveforms from two or more adjacent                windows, each adjacent window residing in a respective                adjacent scan line; and    -   generating an image based on denoised in-band imaging waveforms        respectively associated with the plurality of scan lines.

In another aspect, there is provided a method of denoising imagingsignals detected in the presence of noise, the method comprising:

-   -   for at least two adjacent scan lines:        -   detecting imaging signals with an imaging transducer receive            circuit to obtain an imaging waveform, while also detecting            a reference noise-detection waveform with the reference            receive circuit, and filtering the imaging waveform to            obtain an in-band imaging waveform residing within the            imaging band;        -   segmenting the in-band imaging waveform and the reference            noise-detection waveform according to a series of time            windows;        -   for at least one window:            -   processing the reference noise-detection waveform to                determine whether or not a corresponding windowed                portion of the in-band imaging waveform should be noise                corrected; and            -   in the event that the in-band imaging waveform within                the time window is deemed to be suitable for noise                correction, applying a denoising correction to the                in-band imaging waveform within the time window, wherein                the denoising correction for each sample in the window                is based on one or more statistical measures associated                with samples in the in-band imaging waveforms from two                or more adjacent windows, each adjacent window residing                in a respective adjacent scan line; and    -   generating an image based on denoised in-band imaging waveforms        respectively associated with the plurality of scan lines.

A further understanding of the functional and advantageous aspects ofthe disclosure can be realized by reference to the following detaileddescription and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will now be described, by way of example only, withreference to the drawings, in which:

FIG. 1A shows an example of an ultrasound imaging system configured fornoise suppression.

FIG. 1B shows an example of a conventional ultrasound receive signalchain for the processing of an ultrasound signal before conversion to anultrasound image.

FIG. 1C shows an example of an ultrasound imaging system including anintracorporeal imaging probe.

FIG. 1D shows an example of an ultrasound imaging system including areference transducer for detecting in-band noise.

FIG. 1E shows an example of an ultrasound imaging system including asecond imaging transducer having an imaging band that lies outside theimaging band of a first imaging transducer. The second imagingtransducer may be part of a circuit suitable for detecting in-band noisethat affects the signals received from first imaging transducer.

FIG. 1F shows an example of an ultrasound imaging system including areference receive circuit for detecting in-band noise, where thereference receive circuit extends to a location within the imagingprobe.

FIG. 1G shows an example of an ultrasound imaging system including areference receive circuit for detecting in-band noise, where thereference receive circuit is located in one or more portions of thesystem that are external to the imaging probe.

FIG. 2A illustrates an example system configuration for noise reductionon the envelope of an input waveform via the estimation and suppressionof the estimated in-band noise, where the in-band noise is estimated byperforming envelope detection of an out-of-band waveform, followed bydelay, scale and shape adjustment prior to subtraction.

FIG. 2B illustrates an example system configuration for noise reductionon an input waveform via the estimation and subtraction of in-bandnoise, where the in-band noise is estimated by frequency shifting anout-of-band waveform, filtering the frequency-shifted out-of-bandwaveform, followed by delay, scale and shape adjustment prior tosubtraction.

FIG. 2C illustrates an example of a system configuration for noisereduction with a reference receive circuit that is sensitive to some orall of the in-band noise that the imaging transducer receive circuit issensitive to, but is at least partially isolated from the imagingsignals detected by the imaging transducer receive circuit. Thesubtraction of the noise signals detected by the reference receivecircuit from the signals received by the imaging transducer receivecircuit reduce the noise in the output signal.

FIG. 3A illustrates an example of a system configuration for noisereduction via active noise cancellation, where a cancellation waveformfor active noise cancellation is obtained from a reference receivecircuit.

FIG. 3B illustrates an example of a system configuration for noisereduction on an input waveform via active noise cancellation, where acancellation waveform for active noise cancellation is obtained byenvelope detection of an out-of-band waveform.

FIG. 3C illustrates an example of a system configuration for noisereduction on an input waveform via active noise cancellation, where acancellation waveform for active noise cancellation is obtained byfrequency shifting an out-of-band waveform and filtering thefrequency-shifted out-of-band waveform.

FIG. 4 illustrates an example of a system configuration for noisereduction on an input waveform via filtering of an in-band waveform,where the filtering is controlled based on a feedback parameter obtainedby a filter update algorithm that determines one or more parameters ofthe filter based on one or more characteristics of a noise-detectionwaveform.

FIG. 5A schematically illustrates an example system configuration fornoise reduction on an input waveform based on noise parameters obtainedduring a first measurement stage in the absence of an imaging signal,and by the application of one or more noise reduction algorithms thatutilize the aforementioned noise parameters during a second measurementstage when imaging signals are collected.

FIG. 5B schematically illustrates an alternative example system in whicha reference receive channel is employed to detect in-band noise.

FIGS. 6A and 6C illustrate example system configurations for noisereduction on an input waveform based on detection of noise in anout-of-band waveform, in which different time windows of an in-bandwaveform are suppressed, based on the processing of a respective windowof an out-of-band waveform, and where noise windows of the in-bandwaveform are corrected by subtracting with a subtrahend value dependenton the amount of power within the window of the out-of-band waveform.

FIG. 6B illustrates an example scatter plot showing signal power ofwindows of an in-band waveform versus signal power of respective windowsof an out-of-band waveform in a noise characterization stage.

FIG. 6D illustrates an example system configuration for noise reductionon an input waveform based on detection of noise in an out-of-bandwaveform, in which different time windows of an in-band waveform aresuppressed, based on the processing of a respective window of anout-of-band waveform, and where noise windows of the in-band waveformare corrected by multiplication with an attenuation factor dependent onthe amount of power within the window of the out-of-band waveform.

FIGS. 6E and 6F show charts pertaining to a method in which differenttime windows of an in-band waveform are initially identified as beingpredominantly signal or noise, after which noise windows surrounded bysignal windows are identified as being likely erroneous and arereclassified, and conversely after which signal windows surrounded bynoise windows are identified as being erroneous and are reclassified asnoise.

FIGS. 6G and 6H illustrate an example system configuration for noisereduction on an input waveform based on detection of noise in a filteredreference waveform measured with a reference receive channel, in whichdifferent time windows of an in-band waveform are suppressed, based onthe processing of a respective window of a reference waveform, and wherenoise windows of the in-band waveform are corrected by subtracting witha subtrahend value dependent on the amount of power within the window ofthe filtered reference waveform.

FIG. 6I illustrates an example system configuration for noise reductionon an input waveform based on noise measured in a filtered referencewaveform measured with a reference receive channel, in which differenttime windows of an in-band imaging waveform are corrected, based on theprocessing of a respective window of the filtered reference waveform,and where noise windows of the in-band imaging waveform are corrected byan attenuation factor dependent on the amount of power within the windowof the filtered reference waveform.

FIGS. 7A and 7B illustrate an example system configuration for noisereduction on an input waveform based on noise detected in one or morenoise-detection waveforms, of which at least one noise-detectionwaveform comprises signal that is out-of-band from the imaging band.Different time windows of an in-band waveform undergo noise reductionaccording to one or more patterns identified by processing one or morenoise-detection waveforms.

FIGS. 7C and 7D illustrate an example system configuration for reductionon an input waveform based on noise detected in a reference waveform, inwhich different time windows of an in-band imaging waveform undergonoise reduction according to one or more patterns identified byprocessing one or more reference waveforms.

FIGS. 8A and 8B illustrate an example system configuration for noisereduction on an input waveform based on noise detected in an out-of-bandwaveform, in which different time windows of an in-band waveform undergonoise reduction according to the estimated in-band noise that istemporally aligned prior to reduction.

FIGS. 8C and 8D illustrate an example system configuration for noisereduction on an input waveform based noise detected in a filteredreference waveform, in which different time windows of an in-bandwaveform undergo noise reduction according to the estimated in-bandnoise that is temporally aligned prior to reduction.

FIG. 8E shows an example system configuration for noise reduction on aninput waveform based on noise detected in an out-of-band waveform, inwhich measures from adjacent or replicate scan lines are employed whenperforming noise correction.

FIG. 8F shows an example system configuration for noise reduction on aninput waveform based on noise detected in a filtered reference waveform,in which measures from adjacent or replicate scan lines are employedwhen performing noise correction.

FIG. 9 shows an example of a magnetic resonance imaging systemconfigured for noise suppression.

FIGS. 10A-C show example images obtained using an intra-cardiac echosystem showing (A) an image obtained in the absence of a noise source;(B) an image obtained in the presence of noise generated via anelectroanatomic mapping system; and (C) an image obtained in thepresence of noise generated from an ablation generator.

FIGS. 11A-B show images obtained in the presence of noise from anelectroanatomic mapping system, without (A) and with (B) noisereduction.

FIGS. 11C-E show images obtained in the presence of noise from anelectroanatomic mapping system after noise reduction by attenuation,where the relaxation parameter was set as 0.5 (C), 1 (D) and 1.5 (E).

FIGS. 12A-B show images obtained in the presence of noise from anablation generator, without (A) and with (B) the application of a noisereduction method.

FIGS. 13A-B show the images in obtained in the presence of noise from amagnetic tracking system, without (A) and with (B) the application of anoise reduction method.

FIG. 14 shows the phrases used to refer to waveforms in the imagingband, noise detection band and waveforms from a reference receivecircuit

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosure will be described withreference to details discussed below. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosure.

As used herein, the terms “comprises” and “comprising” are to beconstrued as being inclusive and open ended, and not exclusive.Specifically, when used in the specification and claims, the terms“comprises” and “comprising” and variations thereof mean the specifiedfeatures, steps or components are included. These terms are not to beinterpreted to exclude the presence of other features, steps orcomponents.

As used herein, the term “exemplary” means “serving as an example,instance, or illustration,” and should not be construed as preferred oradvantageous over other configurations disclosed herein.

As used herein, the terms “about” and “approximately” are meant to covervariations that may exist in the upper and lower limits of the ranges ofvalues, such as variations in properties, parameters, and dimensions.Unless otherwise specified, the terms “about” and “approximately” meanplus or minus 25 percent or less.

It is to be understood that unless otherwise specified, any specifiedrange or group is a shorthand way of referring to each and every memberof a range or group individually, as well as each and every possiblesub-range or sub-group encompassed therein and similarly with respect toany sub-ranges or sub-groups therein. Unless otherwise specified, thepresent disclosure relates to and explicitly incorporates each and everyspecific member and combination of sub-ranges or sub-groups.

As used herein, the term “on the order of”, when used in conjunctionwith a quantity or parameter, refers to a range spanning approximatelyone tenth to ten times the stated quantity or parameter.

Ultrasound imaging relies on receiving echoes from a medium, optionallyafter sending a narrow acoustic pulse out in the medium in a particulardirection. As used herein, the term “scan line” refers to a linerepresenting a spatial direction in the medium from which imaging energyis to be received. A 2D image is obtained by receiving echoes from aplurality of scan lines within the medium. The present inventors haveconceived, developed and tested various methods and systems thateffectively reduce broadband noise from an ultrasound acquisition and/orprocessing system.

Referring now to FIG. 1A, an example ultrasound imaging system is shown,in which one or more ultrasound transducers 10 are controlled to performultrasound imaging across a plurality of scan lines 12. The transducer10 interfaces with control and processing hardware 100, which optionallycontrols a transmitter 15 for the generation and emission of imagingenergy by the transducer 10. The control and processing hardware 100 isconfigured to receive ultrasound energy signals detected by thetransducer 10, which are routed, typically via a Tx/Rx(transmit-receive) switch 25, to one or more amplifiers 20.

The ultrasound transducer(s) 10 may optionally be configured to image aspatial region associated with a plurality of scan lines 12, forexample, via mechanical scanning of the transducer 10, or, for example,via electronic scanning via the use of an array of imaging elements,such as, but not limited to, a phased array, ring array, linear array,matrix array or curvilinear array. In the latter case, a transmitbeamformer 26 and receive beamformer 27 may be employed to generate aplurality of transmit signals and to beamform a plurality of receivedsignals.

The term “receive circuit”, as used herein, generally refers tocomponents such as a transmission line (e.g. coax, PCB tracings,others), connectors, mux/demux, RX/TX switches 25, amplifiers 20, sliprings, transformers and other components known in the art.

The term “transducer receive circuit”, as used herein, may include areceive circuit connected to one or more ultrasound transducer elements10 configured to receive ultrasound signals at the time of use.

The phrase “ultrasound receive signal chain”, as used herein, includes areceive circuit, but can include additional components such asanalog-to-digital converters (ADCs) and further digital processingcomponents and/or processing logic, including, but not limited to, noiseremoval processing module 150, before the signal enters the process ofbeing converted into an image (such as via scan conversion) andsubsequent image processing.

As used herein, the term “channel” may refer to conductive electricalcircuits, wireless channels, optical channels, or other signal paths.For example, an imaging receive channel, which denotes the pathtraversed by detected imaging signals, is shown at 13 in FIG. 1A. Thesystem may employ a single receive channel per transducer, or severalreceive channels per transducer (such as may be the case for an arraytransducer where there may a channel for each piezoelectric transducerelement or groups of piezoelectric transducer elements in the array).ASICs and other devices may be used along the signal receive chain tomultiplex signals along a channel from more than one piezoelectrictransducer elements.

In an “imaging mode”, the system may be configured to control thetransducer 10 to optionally transmit energy to a medium, and to detectimaging energy within an imaging frequency band (henceforth referred toas an “imaging band”). The imaging band may constitute a singlecontinuous frequency band, or two or more frequency intervals (such asin harmonic imaging), within which imaging energy is detected. Imagingenergy or noise within the imaging band are henceforth referred to asbeing “in-band”.

FIG. 14 is a representation of the grouping and terminology used todescribe the various waveforms.

The system may also be configured to detect, via one or more channelsconnected to the transducer, energy in one or more additional frequencybands, where at least one frequency band lies, at least in part, beyondthe imaging band. These one or more additional frequency bands arehenceforth referred to as “detection bands”. A waveform that lies, atleast in part, beyond an imaging band is henceforth referred to as being“out-of-band”. In some cases, a detection band may reside within theimaging band. A waveform that lies entirely within the imaging band,with frequency components lying either within the entire imaging band orin sub-bands within the imaging band is henceforth referred to as being“within-band”. Noise-detection bands may be either out-of-band orwithin-band. At least one detection band may be selected such that thesignal-to-noise ratio within the detection band is substantially lessthan the signal-to-noise ratio in the imaging band when the transduceris used in an imaging mode (i.e. when the transducer detects imagingenergy). For example, the detection band may lie outside the full-width,half-maximum bandwidth of the imaging band or another bandwidthcorresponding to a threshold below the maximum strength of the signalemployed.

As used herein, “imaging waveform” refers to a waveform (analog ordigitally sampled) that is obtained from an imaging transducer receivecircuit when the imaging transducer is receiving or is expected to bereceiving imaging energy.

As used herein, the phrase “in-band imaging waveform” refers to animaging waveform (analog or digitally sampled) that lies in the imagingband. An in-band imaging waveform is expected to include imaging energyand may also include unwanted noise energy. In various exampleembodiments of the present disclosure, an in-band imaging waveform isprocessed to remove noise energy for the generation of a denoised image.

As used herein, the phrase “detection-band imaging waveform” refers to awaveform obtained from an imaging transducer receive channel andresiding within one or more noise detection bands. A detection-bandimaging waveform may be out-of-band or within-band. A within-bandnoise-detection imaging waveform may be employed, for example, in orderto confirm the presence of noise within the imaging band. Morespecifically, a “within-band noise-detection imaging waveform” may beemployed to confirm that a noise source having a noise component outsideof the imaging band also has a noise component within the imaging band.A detection-band imaging waveform that lies, at least in part, outsidethe imaging band is referred to as an “out-of-band noise-detectionwaveform”.

Referring to FIG. 1A, the system may be configured to be in a“noise-characterization mode”, during which the transducer 10 does nottransmit energy to a medium, and does not detect imaging energy from themedium.

As used herein, the phrase “noise-characterization waveform” refers to awaveform obtained when the imaging transducer is not receiving imagingenergy.

As used herein, the phrase “in-band noise-characterization waveform”refers to a waveform that resides in the imaging band, obtained from animaging transducer receive channel when the imaging transducer is notreceiving imaging energy.

As used herein, the phrase “detection-band noise-characterizationwaveform” refers to a waveform that resides in a noise detection band,obtained from an imaging transducer receive channel when the imagingtransducer is not receiving imaging energy. A detection-band noisecharacterization waveform that lies, at least in part, outside theimaging band is referred to as an “out-of-band noise characterizationwaveform”. A detection-band noise characterization waveform that liesentirely within the imaging band is referred to as a “within-band noisecharacterization waveform”.

As used herein, the phrase “baseline noise-characterization waveform”refers to a waveform obtained when the imaging transducer is notreceiving imaging energy, and when a selected noise source is expectedto be off (i.e. absent of producing noise), such that the baselinenoise-characterization waveform provides a baseline for the selectednoise source. A baseline noise-characterization waveform that lies in animaging band is referred to as an “in-band baselinenoise-characterization waveform”. A baseline noise-characterizationwaveform that lies in a noise-detection band is referred to as a“detection-band baseline noise-characterization waveform”.

Referring again to FIG. 1A, an optional reference receive circuit 11 maybe provided that includes a receive circuit configured not to receivereflected ultrasound signals during imaging, while being capable ofdetecting noise energy similar to the noise that gets coupled into oneor more transducer receive circuits during imaging. A reference receivecircuit may employ one or more components of a transducer receivecircuit (for example, a reference receive circuit and a transducerreceive circuit may utilize different channels of an amplifier or anADC).

In one example implementation, the system may be configured to detectnoise within the imaging band via one or more reference receivechannels, optionally connected to a reference ultrasound transducer (notshown) that is acoustically isolated or inactivated such that it doesnot transduce reflected ultrasound waves but is sensitive to the noisereceived by the imaging transducer receive circuit. The one or moreimaging transducers 10 and the one or more reference transducers neednot be oriented in a common spatial direction.

The signals received by the one or more reference transducer receivecircuits or reference electrical receive circuits (on a referencereceive channel) are henceforth referred to as reference waveforms. Areference waveform is predominantly noise and not imaging energy.

As used herein, the phrase “reference waveform” refers to a waveformobtained from one or more reference receive channels. A referencewaveform may be filtered to reside within the imaging band and/oroutside the imaging band.

As used herein, the phrase “reference noise-detection waveform” refersto a reference waveform obtained from a reference receive channel whenthe imaging transducer is receiving or is expected to be receivingimaging energy.

As used herein, the phrase “reference noise-characterization waveform”refers to a reference waveform obtained from a reference receive channelwhen the imaging transducer is not receiving imaging energy.

The system may optionally be configured to suppress noise using acombination of detection-band waveforms and reference waveforms. As usedherein, the phrase “noise-detection waveform” refers to either areference waveform or a detection-band waveform. When the system is inimaging mode and the imaging transducer receive circuit is receiving orexpected to receive imaging energy, a noise-detection waveform isreferred to as a “noise-detection imaging waveform”. When the system isin a noise-characterization mode and the imaging transducer receivecircuit is not receiving imaging energy, the noise-detection waveform isreferred to as a “noise-detection characterization waveform”.

Although FIG. 1A shows a single transducer element, it will beunderstood that the embodiment shown in FIG. 1A merely provides but onenon-limiting example configuration, and that transducers with multiplepiezoelectric elements may be employed. For example, in one exampleembodiment, a plurality of transducer elements may be controlled as aphased array or linear array or 2D array. Further, the transducer maynot be limited to one that transmits imaging energy for the purpose ofproducing multi-dimensional 2D cross sectional images or 3D volumes(including 4D imaging datasets comprising 3D images over time), but mayinclude transducers used for Doppler assessment of flow, transducersused as ultrasound beacons (e.g. as described in US Patent PublicationNo. 2016/0045184, titled “Active localization and visualization ofminimally invasive devices using ultrasound”, which is incorporated byreference in its entirety), or ultrasound transducers used to sense theposition of moving elements (e.g. as described in US Patent PublicationNo. 2012/0197113, titled “Ultrasonic probe with ultrasonic transducersaddressable on common electrical channel”, which is incorporated byreference in its entirety).

The transducer may not be limited to one that both transmits andreceives imaging energy as shown in FIG. 1A, but may include transducersthat receive ultrasonic energy from a medium that has been excited byother means, such as an optical energy (photoacoustic imaging), or by aseparate ultrasound transducer. Further, although FIG. 1A shows aconfiguration to image a spatial region associated with a plurality ofscan lines at different directions, the scan lines may beunidirectional, such as in M-mode imaging or during certain Dopplermodalities, such as pulse-wave or continuous wave Doppler to assessflow.

In one example embodiment, a single transducer receive channel isconfigured to receive imaging energy within the imaging band, and tocoincidentally also receive additional energy within one or morenoise-detection bands, of which at least one comprises out-of-bandnoise. In another example, one or more imaging transducer receivechannels may be employed to receive imaging energy within the imagingband, and one or more transducer receive channels may be employed toreceive additional energy within one or more noise-detection bands ofwhich at least one comprises out-of-band noise. In yet another example,one or more reference receive channels may be employed to receive noiseenergy (i.e. reference noise-detection waveforms) while being isolatedfrom the imaging energy in the imaging band. The reference receivechannel may be filtered in a manner similar to the imaging transducerreceive channel by using an imaging band pass filter. Alternatively, inseveral embodiments, the reference receive channel may not be filteredat all, or may have different filters other than an imaging band passfilter to better facilitate estimation of noise within the imaging band.

The control and processing hardware 100 may include, for example, one ormore processors 110, memory 115, a system bus 105, one or moreinput/output devices 120, and a plurality of optional additional devicessuch as communications interface 135, data acquisition interface 140,display 125, and external storage 130.

It is to be understood that the example system shown in FIG. 1A isillustrative of a non-limiting example embodiment, and is not intendedto be limited to the components shown. For example, the system mayinclude one or more additional processors and memory devices.Furthermore, one or more components of control and processing hardware100 may be provided as an external component that is interfaced to aprocessing device. For example, as shown in the figure, an optionaltransmit beamformer 26 and an optional receive beamformer 27 may beincluded as a component of control and processing hardware 100 (as shownwithin the dashed line), or may be provided as one or more externaldevices.

Some aspects of the present disclosure can be embodied, at least inpart, in software, which, when executed on a computing system,configures the computing system as a specialty-purpose computing systemthat is capable of performing the signal processing and noise reductionmethods disclosed herein, or variations thereof. That is, the techniquescan be carried out in a computer system or other data processing systemin response to its processor, such as a microprocessor, CPU or GPU,executing sequences of instructions contained in a memory, such as ROM,volatile RAM, non-volatile memory, cache, magnetic and optical disks,cloud processors, or other remote storage devices. Further, theinstructions can be downloaded into a computing device over a datanetwork, such as in a form of a compiled and linked version.Alternatively, the logic to perform the processes as discussed abovecould be implemented in additional computer and/or machine readablemedia, such as discrete hardware components as large-scale integratedcircuits (LSI's), application-specific integrated circuits (ASIC's), orfirmware such as electrically erasable programmable read-only memory(EEPROM's) and field-programmable gate arrays (FPGAs).

A computer readable medium can be used to store software and data whichwhen executed by a data processing system causes the system to performvarious methods. The executable software and data can be stored invarious places including for example ROM, volatile RAM, non-volatilememory and/or cache. Portions of this software and/or data can be storedin any one of these storage devices. In general, a machine-readablemedium includes any mechanism that provides (i.e., stores and/ortransmits) information in a form accessible by a machine (e.g., acomputer, network device, personal digital assistant, manufacturingtool, any device with a set of one or more processors, etc.).

Examples of computer-readable media include but are not limited torecordable and non-recordable type media such as volatile andnon-volatile memory devices, read only memory (ROM), random accessmemory (RAM), flash memory devices, floppy and other removable disks,magnetic disk storage media, optical storage media (e.g., compact discs(CDs), digital versatile disks (DVDs), etc.), network attached storage,cloud storage, among others. The instructions can be embodied in digitaland analog communication links for electrical, optical, acoustical orother forms of propagated signals, such as carrier waves, infraredsignals, digital signals, and the like. As used herein, the phrases“computer readable material” and “computer readable storage medium”refer to all computer-readable media, except for a transitorypropagating signal per se.

Many of the embodiments described herein employ the adjustment of anoise reduction filter based on noise sensed in the environment. In someexample implementations, one or more of waveforms, data, filterparameters and other pertinent information described in the exampleembodiments below may be transmittable to a network and assessedremotely for further analysis and/or optimization of the noise reductionfilter implementation. Once optimized, the noise reduction filteralgorithms and/or parameters can then be transmitted to the system toenable improved noise reduction.

As shown in FIG. 1A, the example control and processing hardware 100includes an imaging processing module 145 and a noise suppression module150. The image processing engine 145 may be configured or programmed toexecute known image processing methods, such as scan conversion.

While several of the present embodiments are illustrated and describedin a manner that enables real-time noise reduction, it is to beunderstood that the noise reduction could occur in a post-processingfashion. For example, the data on a transducer receive channel or areference receive channel could be digitized and stored before or afterany filtering, envelope detection, shifting, shape/phase or delayadjustments, signal characterization, attenuation, subtraction or othersteps in the described embodiments of the present invention.

FIG. 1B illustrates an example of steps that may be employed by thecontrol and processing hardware 100 and receive channel to process adetected an in-band imaging waveform from an imaging transducer receivechannel prior to image generation. The detected waveform from an imagingtransducer receive circuit may be amplified 201 and filtered 202 priorto analog-to-digital conversion 203. Once digitized, a band-pass filter200 (which may employ multiple pass bands and stop bands) is employed tofilter the detected waveform and retain the signal in the imaging band.The envelope of the filtered waveform is then generated through anenvelope detector 210. The resulting envelope-detected waveform is thenoptionally decimated or expanded 220 and provided to the imageprocessing module 230 for the generation of an image.

Referring again to FIG. 1A, the example control and processing hardware100 includes one or more noise suppression modules 150, which includesinstructions for processing detected data (e.g. raw RF data, envelopedata, or image data) to reduce a contribution of noise, according tonoise reduction algorithms described in detail below. As describedbelow, the noise suppression algorithms disclosed herein (andrepresented in FIG. 1A by noise suppression module 150) may be employedto remove or reduce noise at several potential steps during theprocessing flow shown in FIG. 1B, based on processing one or morenoise-detection waveforms. In the case of systems using arraytransducers, noise suppression may occur either before or afterbeamforming (or both). In various example embodiments described indetail below, noise reduction of imaging data (including, but notlimited to, raw waveforms, sampled waveforms, envelope waveforms,Fourier transformed signals, and processed image data) is performedbased on measurements of signal energy (power, amplitude, intensity, orother measures of signal strength) or waveform patterns of thenoise-detection waveform (such as an out-of-band noise-detection imagingwaveform detected via an imaging receive channel or a referencenoise-detection waveform detected on a reference receive channel). Anoise-detection waveform, which in many embodiments is substantiallyabsent of imaging energy, may contain noise that is correlated with orcoincidental with the noise within the imaging band (or imaging bands).One or more relationships between the imaging band noise and the noisedetected by the noise-detection waveform may be employed for thecorrection of imaging band signals for the removal or reduction (e.g.suppression) of imaging band noise.

Implementation of a Reference Receive Circuit for Intracorporeal Imaging

Referring now to FIG. 1C, an example system is shown for ultrasoundimaging of a region with an intracorporeal imaging probe 350 whichconnects via patient interface module (PIM) 300 to a control andprocessing hardware 100. The intracorporeal ultrasound imaging devicemay be configured to receive acoustic imaging energy from aone-dimensional, two-dimensional or three-dimensional region, optionallyvia mechanical or electronic scanning.

The imaging probe 350 comprises an imaging assembly 353 remote from itsproximal end with an electrical and/or optical channel 354 that passesthrough an optional conduit 354 along at least a portion of its length,and a connector 351 at its proximal end. For the purposes of the presentdisclosure, an imaging assembly 353 generally refers to a component orcollection of components of the imaging probe 350 with which imagingenergy (e.g. acoustic or optical signals) is detected for the purposesof imaging a region that is adjacent to the imaging assembly. Theimaging assembly may optionally include one or more emitters of imagingenergy, and includes at least one receiver of imaging energy. Forexample, the imaging assembly may contain an ultrasonic imagingtransducer 10 that is both an emitter and receiver of acoustic energy.The ultrasonic imaging transducer may be mounted on an imaging assemblythat is optionally attached or otherwise connected to a rotatableconduit or shaft (e.g. a torque cable) 352 housed within a hollow sheathof an intracorporeal ultrasound imaging probe to facilitate mechanicalscanning.

Optional PIM 300 facilitates transmission of signals within any wires orconduits to the appropriate image processing unit 100 via a PIM cable320, such as when the imaging probe 350 does not connect directly to thecontrol and processing hardware 100. The PIM may incorporate one or moreamplifiers 20 to amplify the signals from one or more transducer receivechannels. The PIM may optionally incorporate a motor drive unit 301 thatimparts rotational motion to a rotatable conduit 354. Motor drive unit301 may include slip rings, rotary transformers or other components thatcouple the signals of probe 350 to control and processing hardware 100,thus allowing the imaging conduit to rotate while the PIM cable 320 doesnot. The PIM 300 may also optionally incorporate a pullback mechanism302 or a reciprocating push-pull mechanism to facilitate longitudinaltranslation of the imaging assembly 353. Such longitudinal translationof the imaging assembly may occur in conjunction with the longitudinaltranslation of an external shaft that surrounds the imaging conduit, ormay occur within a relatively stationary external shaft.

Many electrical components within the imaging system may pick upunwanted energy from environmental noise sources. Examples of suchcomponents include the imaging assembly 353, the imaging conduit 352,the motor drive unit 301 and the PIM cable 320. One or more referencereceive circuits that detect noise correlated with the noise detected byan imaging receive circuit may be useful in suppressing in-band noise.The following are example implementations of reference receive circuitsfor noise reduction of an imaging signal in an ultrasound imagingsystem.

FIG. 1D illustrates an example embodiment in which the imagingtransducer is replicated by one or more non-imaging referencetransducers 361 that resides within the imaging probe 350. The referencetransducer has its own electrical channel 360 that passes through theoptional conduit 352 and connector 351. The reference transducer 361 maybe coated with epoxy or with some other acoustic damping material 362 sothat it is acoustically isolated from receiving imaging energy.Alternatively, the piezoelectric may be de-poled to render thepiezoelectric inactive, or may otherwise be substituted with a substratethat is not acoustically sensitive. This implementation may be extendedto array transducers, where there may be a plurality of ultrasoundtransducer elements that are configured to receive acoustic imagingenergy. One or more elements of the array may be acousticallyinsensitive so that it does not transduce acoustic imaging energy andcan function as a reference transducer receive circuit.

FIG. 1E illustrates an example embodiment in which the imaging probecontains two or more imaging transducers, each with a separateelectrical channel, 370 and 354. The two or more imaging transducers maybe sensitive to receive acoustic imaging energy at substantiallynon-overlapping spectral bandwidths. For example, the first transducermay be configured to receive acoustic energy for frequencies around 10MHz, and a second imaging transducer may be configured to receiveimaging energy for frequencies around 40 MHz. The 40 MHz band of the 10MHz-transducer may act as a reference noise channel for the 40MHz-transducer, and similarly, the 10 MHz band of the 40 MHz-transducermay act as a reference noise channel for the 10 MHz-transducer.

FIG. 1F illustrates an example embodiment in which the imagingtransducer receive channel is replicated by a reference receive circuitthat resides within the imaging probe. The reference receive circuit mayoptionally have some combination of resistors, inductors, capacitorsand/or other components, configured such that electrical impedance ofthe reference electric circuit 381 is matched to the impedance of theimaging transducer receive circuit or such that the sensitivity of thereference electric circuit to noise is rendered more similar to thesensitivity of the transducer receive channel to noise. The advantage ofsuch embodiments is that they may be less expensive, easier tomanufacture and easier to miniaturize some of the components ofreference electric circuit 381 by not requiring an actual ultrasoundtransducer. Furthermore, a portion of the reference receive circuit mayserve additional purposes, such as transmitting energy to drive anactuator (such as, but not limited to, a magnetic actuator), or carryinga signal (including, but not limited to temperature, pressure or currentgenerated from an electromagnetic field for position sensing). Such useof the reference receive circuit for additional purposes other thansolely collecting a reference noise-detection waveform to reduce noisein the imaging signal may allow for easier miniaturization, lower costsand/or improved functionality.

FIG. 1G illustrates an example embodiment in which the reference receivecircuit 391 terminates within the PIM where, similar to the imagingreceive channel, it is subject to noise received by the motor drive unitand the PIM cable. Here only a portion of the transducer receive circuitis replicated. Reference noise detection may optionally be used incombination with detection-band (out-of-band or within-band) noisedetection to further reduce noise of an in-band imaging waveform from animaging transducer receive channel.

It is noted that the embodiments in which a detection-band waveform isemployed as the noise-detection waveform may be less costly to producethan those that employ the use of a reference circuit or referencetransducer to generate a reference waveform, as the former does notrequire the physical implementation of a reference channel, such as incases where an imaging probe, or part thereof is not used repeatedlyacross different patients. It is also noted that a noise-detectionwaveform from a reference channel may be more effective in some imagingsystems at reducing noise, as it can provide information about noisethat resides within the imaging band, whereas an out-of-bandnoise-detection waveform does not provide a direct estimate of thein-band noise and instead relies on noise whose in-band properties canbe predicted based, at least in part, on its out-of-band properties.

It is also noted that the noise estimates obtained using either adetection-band waveform or a reference noise-detection waveform may beused to reduce noise in more than one imaging channel. For example, in aphased array transducer, where there are a plurality of piezoelectricelements, it is possible to use a single reference receive channel or asingle out-of-band noise-detection waveform to estimate noise that mightbe collected by all or a subset of the piezoelectric elements, and thusapply the same noise estimation scheme to the signals collected from allor a subset of the piezoelectric elements.

Noise Measurements

The following sections of the present disclosure describe severaldifferent example embodiments for performing noise reduction of in-bandimage data based on out-of-band noise detection, or reference channelnoise detection, or a combination of both out-of-band noise detection(possibly supplemented further by in-band noise detection) and referencechannel noise detection.

As will be described in relation to the following illustrative exampleembodiments, various noise measurements and/or noise characteristics maybe determined from the measurements in the noise-detection waveform inorder to increase the signal-to-noise ratio within the imaging band.Non-limiting examples of noise measurements include any one or more of:

-   -   measurements of energy (amplitude, root-mean-square amplitude,        average power) in the noise-detection imaging waveform and the        in-band imaging waveform;    -   measurements of energy in two or more different noise-detection        bands;    -   temporal, spectral and/or time-frequency properties of the        noise-detection waveform; or spatial or spatio-temporal patterns        in images created using a noise-detection waveform, including        features or parameters that describe or characterize such        patterns;    -   temporal, spectral and/or time-frequency properties of in a        noise-detection imaging waveform and co-incidental patterns in        the in-band imaging waveform, including features that describe        these patterns; spatial or spatio-temporal patterns in images        created using a noise-detection waveform and co-incidental        spatial patterns in images created from the in-band imaging        waveform from an imaging transducer receive channel, including        features or parameters that describe or characterize such        patterns;    -   filter parameters that are determined or controlled using        parameters obtained based on waveform characteristics, such as        energy, and spectral spacing of harmonic peaks, detected in a        noise-detection waveform.

In some example embodiments, the estimation of noise characteristics maybe performed when an imaging transducer is not receiving imaging energy(e.g. after ultrasound energy from the most recent emission of anultrasound pulse is expected to have been extinguished from theenvironment). Alternatively, the estimation of noise characteristics maybe performed during imaging, when imaging energy is expected to bedetected (e.g. when the transducer is in image acquisition mode). Insome example embodiments, in which noise characteristics are measured inthe absence of imaging energy, such noise characteristics may be updatedintermittently in order to adapt to and compensate for time-dependentchanges in the noise characteristics.

Noise Reduction Using Measurements without Imaging Energy Present

Although some noise reduction embodiments involve the measurement anduse of energy from a noise-detection waveform while the imagingtransducers are receiving imaging energy, alternative embodiments mayemploy measurement from a noise-detection waveform that are obtainedduring time periods in which there is an absence of imaging energy, or acombination of both.

FIG. 5A illustrates an example of such an embodiment, in which noisecharacterization is performed while the transducer receive channel isnot receiving imaging energy, and where the resulting noisecharacterization can be employed to enable noise reduction of in-bandimaging waveforms acquired while the transducer receive channel isreceiving imaging energy. Typically, the noise characterization stepwould occur prior to the acquisition and processing of imaging data thatis noise reduced, but with appropriate recording of the imaging data,the information gathered during noise characterization could be employedpost-hoc on the recorded imaging data.

According to the present example method, energy is detected in atransducer receive circuit during a first time window when it is eitheranticipated or known that at least one transducer receive circuit is notreceiving imaging energy, such that the waveform detected by an imagingtransducer receive channel 13 is deemed to be noise 405. The detectedwaveform is filtered at 200 and 410 to generate an in-band noisecharacterization waveform 407 and an detection-band noisecharacterization waveform 408.

The in-band noise characterization waveform and the detection-band noisecharacterization waveform are processed to characterize their noiseproperties, as shown at 420. The noise characterization 420 may beemployed, for example, to generate characteristic parameters 430 thatcharacterize the noise. Examples of suitable noise characterizationparameters are provided in the forthcoming example embodiments.

Optionally, energy may be detected in a transducer receive circuitduring an additional baseline noise characterization stage when it iseither anticipated or known that at least one transducer receive circuitis not receiving imaging energy, and a certain noise source isselectively known to be off, such that the waveform detected by animaging transducer receive channel 13 is deemed to be baseline noise fora selected noise source. The detected baseline noise characterizationwaveform is filtered at 200 and 410 to generate an in-band baselinenoise characterization waveform 407 and a detection-band baseline noisecharacterization waveform. It is to be understood that noise parameters430 may include parameters obtained during a baseline noisecharacterization stage.

Characteristic noise parameters may be calculated before or during animaging session, or may be retrieved from a pre-stored database locatedon a local or remote storage drive (network drive, cloud, etc.).

After having characterized the noise in the absence of imaging energy,the characteristic parameters 430 may be employed to perform noisereduction of the in-band imaging waveform 437 detected while thetransducers are receiving imaging energy. The waveform detected from animaging transducer receive channel during imaging 435, containingimaging energy and noise, is filtered at 200 and 410 to generate anin-band imaging waveform 437 and a detection-band imaging waveform 438.Therefore, the in-band imaging waveform 437 contains detected imagingenergy and noise, and the detection-band imaging waveform 438 containsinformation associated with the likely presence of noise in the in-bandimaging waveform 437. The characteristic parameters 430 obtained duringthe noise characterization stage may then be employed for the detectionand/or estimation of noise 440 within the in-band imaging waveform, andto perform noise suppression 500 of the in-band imaging waveform.Examples of suitable noise characterization parameters are how they areused to reduce noise are provided in the forthcoming exampleembodiments.

FIG. 5B illustrates an alternative embodiment in which a referencereceive channel, such as the reference receive channel described withreference to FIG. 1A, is employed to detect noise and generate areference noise characterization waveform 406. This reference noisecharacterization waveform is filtered at 202 to generate a filteredreference noise characterization waveform 409. In one exampleembodiment, the reference channel filter could be an imaging band passfilter. Alternatively, if the noise estimation benefits from input ofout-of-band noise, then the reference channel filter may be differentfrom an imaging band pass filter. The reference noise characterizationwaveform 409, and optionally the in-band noise characterization waveform407, are processed at step 420 to provide noise characterizationparameters 430.

During imaging, a reference waveform 436 is detected and optionallyfiltered to generate a filtered reference noise-detection waveform 439.The characteristic parameters 430 obtained during the noisecharacterization stage may then be employed for the detection and/orestimation of noise 440 within the in-band imaging waveform 437, and toperform noise suppression 500 of the in-band imaging waveform 437. Inanother example embodiment, both the detection-band imaging waveform 438(shown in FIG. 5A) and the filtered reference noise-detection waveform439 are processed to provide information about the likely presence ofnoise in the in-band imaging waveform 437.

In some example embodiments, noise suppression may be achieved byprocessing the in-band imaging waveform using one or more of thefollowing methods: subtracting the estimated noise from the signal inthe imaging band; attenuation of the estimated noise energy bymultiplying the signal in the imaging band with an attenuation factor;and filtering the signal in the imaging band. For example, a subtrahendvalue may be proportional to the amount of power detected within theout-of-band noise-detection imaging waveform. In another example, anattenuation factor may be inversely proportional to a measure associatedwith the amount of noise in the in-band imaging waveform, therebyattenuating portions of the in-band imaging waveform associated withnoise.

Noise characterization (as shown, for example, at 420 in FIGS. 5A-B) maybe performed once, or may alternatively be performed multiple times, orcontinuously. For example, noise characterization may be performedintermittently over time (e.g. at periodic or aperiodic intervals) inorder to adapt to, and to compensate for, time-dependent changes in thenoise characteristics.

When noise characterization is occurring, the noise characterizationwaveforms collected for noise characterization may digitized andcollected in multiple discrete arrays, such as arrays that are each longenough to store imaging data along a single scan line in ultrasoundimaging, or they may be collected in a more continuous fashion as one ormore data streams that get stored into a large array, a circular bufferor other data structure.

In some example implementations, noise characterization may be userinitiated (e.g. by pressing a button), for example, at the start of animaging session or when the user observes or suspects a degradation inimage quality.

In other example implementations, noise characterization may betriggered, such as either automatically or after prompting the user,when the absence of imaging energy is detected. For example, periods ofabsence of imaging energy may be detected when the relative energybetween an in-band imaging waveform and a noise-detection imagingwaveform is unchanged for a prescribed period of time and lies within apredefined range. Periods of absence of imaging energy may also bedetermined, for example, when the energy in the in-band imaging waveformafter noise correction at 501 is below a predefined threshold,indicating the absence of imaging energy.

The noise characterization step 420 may also be useful in alerting theuser or the system that the noise profile has changed in a manner thatmay cause the noise reduction algorithm to adversely affect the system(if a new isolated source of noise is detected, the noise suppressionmodule 500 may erroneously cause suppression of the in-band signal or beless effective at suppressing in-band noise). For example, anoise-detection waveform (e.g. the out-of-band noise-detection waveform438 of FIG. 5A or a reference noise-detection waveform 439 of FIG. 5B)may be processed to determine that the noise characteristics havechanged. For example, while performing noise reduction, an optionalnoise monitoring module could be employed that monitors thecharacteristics (such as peak energy, power, frequency content slope,skew, kurtosis, histogram or some other metrics) of the noise-detectionwaveform. If the characteristics of a noise-detection waveform change(e.g. if the peak energy exceeds a threshold value), the noisemonitoring module can communicate with other parts of the system (suchas via a message, interrupt, alarm or other) to alert that the noisecontent has changed.

In another implementation, an error value may be evaluated in a noisecharacterization stage, where noise suppression is performed on thein-band imaging waveform in the absence of imaging energy and the errorvalue is the energy of the in-band imaging waveform after noisecorrection. If the error value exceeds a pre-defined threshold, an alertis generated. An alert may prompt a re-characterization of the noise, orthe system may choose to ignore one or more out-of-band noise-detectionbands or one or more reference receive channels in its noise removalalgorithm.

Noise characterization 420 may also be useful in determining the noisesources in the environment. Noise sources may be determined, forexample, by a pattern recognizer, such as one described in step 570 ofFIG. 7A (described in greater detail in Embodiment 4). Information onthe noise sources can be used, for example, in order to access adatabase (local or networked) to select parameters for noise suppressionor in order to determine the sequence of noise reduction methods to beused. For example, it may be preferably to remove periodic noise first(as described in detail in Embodiment 6 FIG. 8A-D), followed by lessperiodic noise.

As a further example, noise characterization may be useful at detectingthe type of electroanatomic mapping system being used during an ablationprocedure, or detecting the activation and deactivation of an ablationcatheter, such as a catheter that uses radiofrequency energy to performablation to treat arrhythmias. This may be achieved, for example, by apattern recognizer such as one described in step 570 of FIG. 7A. The oneor more noise-detection waveforms may further be capable of detectingthe duration or the relative intensity or frequency of the ablationenergy being applied. Such information could be useful to anintracardiac imaging system, as it may facilitate annotation of animaging dataset with information about when a noise source, such as anablation catheter, was activated during a procedure.

Noise suppression (as shown, for example, at 500 in FIGS. 5A-B) may beperformed once, multiple times, intermittently or continuously. Forexample, noise suppression may be user initiated. Alternatively, noisesuppression may be performed intermittently over time (e.g. at periodicor aperiodic intervals) in order to compensate for time-dependent noisesource. In another example, noise suppression may be controlled by theexternal device that emanates the noise. For example, noise suppressionmay be enabled or disabled by the controls of an RF ablation generator,such that noise suppression is performed when RF energy is beingdelivered.

The following embodiments are described for one imaging waveform and onenoise-detection waveform. It is to be understood that these embodimentsmay be extended to a plurality or imaging waveforms and/or a pluralityof noise-detection waveforms.

Embodiment 1: Noise Reduction Based on Suppressing Envelope DetectedOut-of-Band Noise with Optional Amplitude, Shape and Delay Correction

Referring now to FIG. 2A, an example method is illustrated in which theout-of-band noise-detection waveform 438 is employed to perform noisereduction of the in-band imaging waveform 437 via a suppression operator525. In one example embodiment, a suppression operator may be asubtractor that subtracts the estimated noise from the in-band imagingwaveform 437. In another example embodiment, the suppression operatormay be an attenuator that attenuates the in-band imaging waveform withan attenuation factor that is derived from the estimated noise. One ormore transducer receive channels are employed to detect imaging energy,where the energy detected includes both the imaging band and thenoise-detection band. The waveforms can be digitally sampled, split (orcopied) and filtered thereby obtaining a sampled in-band imagingwaveform 437 and a sampled out-of-band noise-detection imaging waveform438. The sampled waveforms may be detected as a set of samples that arereceived in a time window (listening window). For example, in the caseof ultrasound imaging, the listening window may occur immediately orshortly after pulsing an ultrasound transducer such that it emits energyinto the adjacent environment. Pulsing could correspond to sending outone or more pulses.

In the example embodiment shown in FIG. 2A, the imaging waveform 435 isfiltered (digitally or analog) using an imaging band pass filter 200 anda noise-detection-band filter 410 that spans frequencies outside theimaging band. Envelope detection may then be performed on the in-bandimaging waveform 437 and out-of-band noise-detection imaging waveform438, as shown at 210 and 411, respectively.

In the example embodiment shown in FIG. 2A, the out-of-bandnoise-detection imaging waveform 438 is employed to reduce noise of thein-band imaging waveform 437. Prior to suppression, the amplitude of theenvelope-detected out-of-band noise-detection imaging waveform 438 isoptionally scaled via an amplitude adjustment factor, as shown at 510,in order to compensate for differences in the noise power within theimaging band and the noise-detection band. In one exampleimplementation, the amplitude adjustment factor may be determined basedon the power spectrum of the noise, as determined in the absence ofimaging energy, i.e. in a noise characterization stage. In anotherexample implementation, the amplitude adjustment factor may be selected,or modified, by an operator, in order to provide a desired level ofnoise reduction, or determined after cross correlation at 580 (describedbelow).

Prior to suppression, the envelope-detected out-of-band imaging waveformmay be temporally dilated, compressed or shape-adjusted using some otherlinear or non-linear temporal scaling function, as shown at 510, inorder to compensate for differences in the shape of the noise waveformsbetween the imaging band and the noise-detection band.

As shown at 510, it may also be beneficial to apply a delay correctionto the envelope-detected out-of-band noise-detection imaging waveformprior to suppression. For example, the two bandpass filters 200 and 410may not transform the input waveforms similarly. Either the band passfilters or the properties of the noise itself may result in an offset ofthe noise as it propagates through the band pass filters. In the absenceof a delay correction, the noise may be erroneously shifted prior tosuppression, which can negatively impact the noise reduced signal.

In one example implementation, delay adjustment may be achieved bycalculating a cross-correlation between the in-band imaging waveform andthe out-of-band imaging waveform, and aligning the waveforms at thepoint where the cross-correlation is maximum. In other words,cross-correlation can be employed to determine a time delay correctionvalue for correcting the relative temporal misalignment of the envelopesof the in-band imaging waveform and the out-of-band imaging waveform.

The time delay correction value and the amplitude correction value maybe calculated using a plurality of sampled in-band imaging waveforms and(co-incidental) sampled out-of-band noise-detection imaging waveforms(which may be referred to as “arrays”), or in one or more time windowsin a sampled in-band imaging waveform and (co-incidental) a sampledout-of-band noise-detection imaging waveform.

In one example implementation, out-of-band noise may be interrogated asa plurality of noise-detection bands, and the dependence of the power onfrequency among the plurality of noise-detection bands may be employedto select a suitable amplitude adjustment for estimating the noise powerthat is present in the imaging band for suppression at step 525. Forexample, the average noise power within multiple noise-detection bandsmay be fitted to a functional dependence on frequency, such as a linearfit, in order to estimate the noise power within the imaging band. Thisfunctional dependence on frequency may be determined in the absence ofimaging energy i.e. in a noise characterization stage 420.

Alternate Embodiments: Noise Reduction Based on Using Frequency ShiftedOut-of-Band Noise-Detection Waveforms with Optional Amplitude, Shape andDelay Correction

FIG. 2B illustrates an alternative example embodiment of a subtractiveor signal-attenuating noise correction method in which the out-of-bandnoise-detection imaging waveform is frequency-shifted prior to delay andamplitude adjustment. As shown at 530, a frequency shift operation (forexample, by multiplying by a complex exponential) is performed on anout-of-band noise-detection imaging waveform, shifting the spectrum ofthe out-of-band noise-detection imaging waveform so that it lies within,or overlaps with the imaging band.

The frequency shift operation 530 may be performed such that the centerfrequency of the frequency-shifted noise-detection waveform coincides,or is approximately equal to, the center frequency of the imaging band.For example, if the imaging band ranges from 7-13 MHz, the centerfrequency is f_(c1)=10 MHz. If the noise-detection band ranges from15-25 MHz, the center frequency of the noise-detection band is f_(c2)=20MHz. Accordingly, the frequency shift operation may be performed suchthat the out-of-band noise-detection imaging waveform is shifted byf_(c2)−f_(c1)=−10 MHz. Alternatively, the frequency shift operation maybe performed such that the center frequency of the frequency-shiftedout-of-band noise-detection imaging waveform coincides, or isapproximately equal to a frequency within the imaging band where it isanticipated or known that a portion of the in-band noise resides.

After frequency shifting, another stage of band pass filtering 203 isperformed to filter out the sum-frequency artifact (in theaforementioned example, the sum frequency is f_(c2)+10 MHz=30 MHz).Frequency shifting may be advantageous over the envelope detectionembodiment illustrated in FIG. 2A, because frequency shifting may resultin better correlation between the noise in the imaging band and thenoise-detection band, which may result in better noise suppression. Itis noted that in the example embodiment shown in FIG. 2B, envelopedetection 210 may be applied to the output signal 501 after it hasundergone noise reduction to obtain a noise-reduced signal envelope 520.

Alternative Embodiments Based on Use of a Reference Noise Signal

Referring now to FIG. 2C, an example method is illustrated in whichin-band noise, detected via a reference receive channel (using areference receive circuit) is employed to reduce noise in the in-bandimaging waveform via a suppression operator (i.e. subtractor orattenuator). One or more imaging transducer receive channels areemployed to receive imaging energy, and one or more reference receivechannels are employed to receive noise energy (i.e. reference waveforms)that is anticipated to correlate with the noise energy received by theimaging transducer receive channel.

The waveforms may be digitally sampled, thereby obtaining sampledin-band imaging waveforms and sampled filtered reference noise-detectionwaveforms. Alternatively, the noise suppression could be performed withanalog electronics, such as by using an analog signal adder with theinput into the adder from the reference receive circuit being invertedin the delay, scale and shape adjustment block 510, thus resulting insubtraction of the estimated noise. As yet a further alternateembodiment for analog signal suppression, the suppression can beembodied as an amplifier with a time-varying gain, wherein the gain ismodulated by the noise detected in the reference receive channel.

In the example embodiment shown in FIG. 2C, the input waveforms from animaging transducer receive channel and the reference receive channel arefiltered (digitally or analog) using an imaging band pass filter 200,and optional reference channel filter 202 thereby providing the in-bandimaging waveform and a filtered reference noise-detection waveform,respectively. As mentioned previously, reference channel filter 202 maybe similar to the imaging band pass filter 200. Envelope detection isthen optionally performed on the filtered signals, as shown at 210 and411. In the example embodiment shown in FIG. 2C, the referencenoise-detection waveform, measured by the reference receive channel, isemployed to reduce noise in the in-band imaging waveform. Prior tosubtraction from the in-band imaging waveform (or its envelope) orattenuation of the in-band imaging waveform (or its envelope), theamplitude of the filtered reference noise-detection waveform (or itsenvelope) is optionally scaled via an amplitude adjustment factor, asshown at 510, in order to compensate for differences in the noise powerwithin the filtered reference noise-detection waveform and the in-bandimaging waveform. In one example implementation, the amplitudeadjustment factor may be determined based on the power spectrum of thenoise, as determined in the absence of imaging energy i.e. in a noisecharacterization stage. In another example implementation, the amplitudeadjustment factor may be selected by an operator in order to provide adesired level of noise reduction or determined after cross correlationat 580.

As shown at 510, it may also be beneficial to apply a delay correctionto the envelope-detected filtered reference noise-detection waveformprior to subtraction. In one example implementation, delay adjustmentmay be achieved by calculating a cross-correlation between the in-bandimaging waveform and the filtered reference noise-detection waveform,and aligning the waveforms at the point where the cross-correlation ismaximum. Similar to the previous embodiment, the time delay correctionvalue and the amplitude correction value may be calculated using aplurality of in-band imaging waveforms and (co-incidental) referencenoise-detection waveforms or one or more windows of an in-band imagingwaveform and a (co-incidental) reference noise-detection waveform.

Embodiment 2: Noise Reduction Using Reference Noise-Detection Waveformfrom a Reference Receive Channel as Input to Adaptive Filter

FIG. 3A illustrates an example embodiment of a noise correction methodin which an adaptive filter is employed, in an active noise control(ANC) scheme, by applying a noise reducing correction to the in-bandimaging waveform based on a reference noise-detection waveform, wherethe reference noise-detection waveform is correlated with the noisedetected by an imaging transducer receive circuit. The referencenoise-detection waveform is filtered at 202 and the waveform from theimaging transducer receive channel is filtered at 200. In a preferredembodiment, the in-band imaging waveform and the referencenoise-detection waveform are filtered within the same band (e.g. 7-13MHz for an exemplary intracardiac echocardiography imaging system).

An adaptive filter is a linear filter that has a transfer functioncontrolled by variable parameters and a means to adjust those parametersaccording to an optimization algorithm. Adaptive filters are typicallydigital finite-impulse-response (FIR) or infinite-impulse-response (IIR)filters. An active noise control (ANC) scheme is provided for theprimary input which receives a signal (S) from the signal source that iscorrupted by the presence of a noise (N) that is uncorrelated with thesignal. The reference input receives noise (N_(r)) that is uncorrelatedwith the signal but is correlated in some way with the primary inputnoise (N). The reference noise passes through an adaptive filter toproduce an output noise (N_(estimate)) that is an estimate of theprimary input noise (N). The noise estimate is subtracted from thecorrupted signal to produce an estimate of the noise reduced signal(S_(estimate)) The adaptive filter actively adjusts its coefficients tominimize the output power E[S_(estimate) ²]. Since the signal S isuncorrelated with N and N_(r), while noise N is correlated with noiseN_(r), minimizing the total output power maximizes the signal-to noiseratio. Minimization algorithms, such as a stochastic Least Mean Squares(LMS) algorithm or the deterministic Recursive Least Squares (RLS)algorithm may be used to find filter coefficients that minimize theoutput noise power.

In the example ANC scheme shown in FIG. 3A, the reference noise ismeasured via a reference receive channel using a reference receivecircuit that is isolated from the imaging energy. The primary input isobtained by applying an imaging band pass filter 200 to an inputwaveform from an imaging transducer receive channel 13. A referenceinput N_(r) is obtained by applying a reference channel filter 202 tothe reference noise-detection waveform obtained via a reference receivechannel.

Alternative Embodiments: Active Noise Cancelling Using Out-of-Band Noise

Unlike the form of active noise control described above where thereference noise-detection waveform and the in-band imaging waveform maybe detected and processed within overlapping frequency bands (andpotentially a common frequency band), FIGS. 3B, and 3C illustrateexample embodiments of noise correction methods in which an adaptivefilter 540 is employed to apply a noise reducing correction to thein-band imaging waveform using an out-of-band noise-detection imagingwaveform.

The primary input is obtained by applying an imaging band pass filter200 to an input waveform from an imaging transducer receive channel, andobtaining its envelope 210. In example embodiments illustrated in FIGS.3B and 3C, an out-of-band noise-detection imaging waveform is obtainedby applying a detection band filter 410 to an input waveform from animaging transducer receive channel.

In FIG. 3B, the out-of-band noise-detection imaging waveform isdemodulated via envelope detection 411 (in a manner similar to theembodiment shown in FIG. 2A) to obtain a reference input for ANC.

In FIG. 3C, the out-of-band imaging waveform is frequency-shifted (at530) to the imaging band (e.g. 7-13 MHz) in a manner similar to theembodiment shown in FIG. 2B and filtered using an imaging band passfilter 203 to obtain a reference input for ANC.

Embodiment 3: Noise Reduction Based on Frequency Shift, UsingDetection-Band Waveform as Input to Variable Filter of In-Band Waveform

FIG. 4 illustrates an example embodiment of a noise correction method inwhich a dynamic filter 550 is employed to filter the in-band imagingwaveform, where the dynamic filter is controlled by a filter updatealgorithm 560 that updates filter coefficients after processing anout-of-band noise-detection imaging waveform that includes out-of-bandnoise, and optionally, a within-band noise detection imaging waveformthat includes noise within the all or part of the imaging band. As inprevious embodiments (FIG. 2B and FIG. 3B), as shown in FIG. 4 , theinput waveform is separately filtered with an imaging band pass filter200 and a noise-detection band-pass filter 410, thereby generating anin-band imaging waveform 437 and at least one out-of-bandnoise-detection imaging waveform that includes out-of-band noise 438.The one or more out-of-band noise-detection imaging waveforms areprocessed by a filter-update algorithm at 560.

The filter-update algorithm analyzes the out-of-band noise and mayevaluate signal characteristics, such as by performing a Fouriertransform on a waveform array and identifying spectral maxima and thefrequencies at which they occur. The filter update algorithm may usethis information to control the coefficients of a dynamic digital filterthat filters the imaging waveform at 550.

In one example implementation, the present method may be employed toreduce noise in a signal containing harmonic noise. For example,harmonic noise may be generated from a switching rectangular pulsesource where, in frequency domain, the spacing of spectral lines isdependent on the pulse repetition frequency. If thepulse-width-modulation source generates noise that spans the 3-40 MHzband, where the imaging band lies within 7-13 MHz, and the dynamicfilter 550 is a comb or multiple-notch filter, the filter updatealgorithm 560 may process signals from the noise-detection band (e.g.evaluate spectral line spacing and locations as seen in the 15-25 MHzrange) and may use this information to control the stop bands in thedynamic in-band filter 550 to remove or reduce the harmonic noise.

In addition to out-of-band noise being used to update the dynamicfilter, filter update algorithm 560 and/or dynamic filter 550 mayoptionally also probe the a within-band noise-detection imaging waveformto confirm the presence of in-band noise at one or more selectedsub-bands within the imaging band prior to removing or reducing noise.For example, if the imaging band lies within 7-13 MHz and the filterupdate algorithm recognizes that there is harmonic noise at 15 MHz, 18MHz, 21 MHz, 24 MHz and 24 MHz (integer multiples in the 15-25 MHzrange), then the filter update algorithm may set the dynamic filter tofilter out signals at 9 MHz and 12 MHz (integer multiples of 3 MHzwithin the imaging band). In one example implementation, such a filtermay optionally only be applied if the presence of signal at 9 and 12 MHzis greater than expected relative to other signals within the imagingband are confirmed. In another example implementation, a noisecharacterization step may be performed, in the absence of imagingenergy, to determine whether or not harmonic noise is present within theimaging band.

Embodiment 4: Noise Reduction Based on Pattern Recognition

FIG. 7A and FIG. 7B illustrate an example embodiment of a noisecorrection method in which pattern recognition is employed to detectnoise and to perform noise reduction on in-band imaging waveforms. Inthis example embodiment, matched sets of patterns in detection-bandnoise-characterization waveforms (at least one of which is lies, atleast in part, outside the imaging band) and related in-bandnoise-characterization waveforms are initially identified during a noisecharacterization period in the absence of receiving imaging energy, asshown in FIG. 7A. Having correlated noise-detection patterns within-band noise patterns via the noise characterization stage, thesecorrelations may be employed during imaging to perform noise reductionof in-band imaging waveforms, based on the identification of patterns inthe one or more detection-band imaging waveforms.

According to a first stage of the present example method, in a noisecharacterization stage, energy is detected within both an imaging band(in-band) and a noise-detection band in the absence of imaging energy(such as during a non-imaging noise characterization stage), therebyobtaining correlated measurements of in-band and detection-bandnoise-characterization waveforms. Samples from the in-bandnoise-characterization waveform and detection-bandnoise-characterization waveform are recorded as pairs of arrays, wherean array pair refers to a sampled in-band waveform and a secondcorresponding sampled detection-band noise-detection waveform recordedat the same time.

The detection-band noise-characterization array and the in-band noisecharacterization array may be windowed at 566 and 565. The windows maybe sliding windows, with optional overlap. Optionally, the windows maybe centered around a peak noise amplitude, or be time-locked to a noiseamplitude threshold. The windows may also be conditioned to reduceartifacts induced by windowing, such as by applying a window function,such as a Hamming window, Blackman window or other window functions wellknown in the art of signal processing. The array data (or windowsthereof) may be processed to identify the presence of one or more noisepatterns at 570.

Referring now to FIG. 7A, one or more detection-bandnoise-characterization arrays are processed to identify waveformpatterns associated with patterns in the in-band noise-characterizationwaveforms. The pattern recognizer in step 570 extracts features from thedetection-band characterization arrays and uses a predictive model toclassify the features into noise ‘classes’. The extracted features couldbe statistical features (including, but not limited to, variance,standard deviation, power, skewness, and kurtosis) in time domain,frequency domain (e.g. peak frequency), time-frequency domain (e.g.wavelet coefficients). The choice of features to be extracted may bemade beforehand using feature selection algorithms such as forwardselection or backward elimination methods.

Extracted features are fed to the predictive model in step 570, whichmay be trained to identify a pattern in a detection-band waveform andassign the pattern to a noise class. For example, machine learningmethods may be used to train the predictive model to recognize patternsin the detection-band noise-characterization array using the extractedfeatures. The predictive model may include an unsupervised learningmodel (such as k-means clustering), or a supervised learning model (suchas a linear classifier, an artificial neural network or anearest-neighbor classifier). Supervised learning may be used if priorinformation about noise sources are known, for example, the sources andsequence of the noise patterns may be known beforehand and noise classlabels may be assigned to a waveform pattern in the detection-bandnoise-characterization waveform. The predictive model in step 570 mayalso accept as input class weights or a priori probabilities. The higherthe a priori probability or weight of a class, the more likely it is tobe recognized.

A database, shown in step 575 may store in-band noise-characterizationwaveform patterns that are known to be co-incidental with detection-bandnoise-characterization waveform patterns. For example, the database maystore exemplary or average temporal in-band noise-characterizationwaveform patterns, paired with features of the co-incidentaldetection-band noise characterization patterns and noise class labels.The detection-band noise-characterization waveform patterns and theircoincidental in-band noise-characterization waveform patterns may bedetermined on a per-window basis or otherwise. Further noisecharacterization may be performed in the temporal domain, or in thespatial domain after image generation at steps 230 and 231, in whichcase spatial features may also be extracted in step 570. The databasemay be of any suitable format used in computing, such a lookup tables.

Having correlated detection-band noise patterns with in-band waveformnoise patterns via the noise characterization stage described above,these correlations may be employed during imaging to perform noisereduction of in-band imaging waveforms, based on the identification ofpatterns in the one or more detection-band imaging waveforms.

Referring to FIG. 7B, an in-band imaging waveform, is obtained byapplying an imaging-band bandpass filter 200 to a waveform detected byan imaging transducer receive channel while the transducer is receivingimaging energy, and optionally performing envelope detection 210. Adetection-band imaging waveform, is obtained by applying anoise-detection bandpass filter 410 to a waveform detected by an imagingtransducer receive channel, and optionally performing envelope detection411. The detection-band imaging waveforms and the in-band imagingwaveforms may be windowed at 566 and 565, similar to the windowing stepin the noise characterization stage. The waveforms may be sampled andrepresented as arrays.

Features may be extracted from the detection-band imaging array (orwindows thereof), similar to the feature extraction step in thenoise-characterization stage. Extracted features, and optionally classweights, are employed by the pattern recognizer 570, trained in thenoise-characterization stage (described above), to identify the presenceof one or more patterns in the detection-band imaging waveform.

The period (i.e. repetition frequency) of a noise pattern may be used toadjust the a priori probability of that pattern class (e.g. for a Bayesclassifier) or to adjust the weight of that class (e.g. for a SupportVector Machine) while applying a pattern classification algorithm in570. The higher the a priori probability or weight of a class, the morelikely it is to be recognized. If the repetition interval of a patternis known, the pattern is expected to be present at given times with ahigher probability. The a priori probability or weight of that classcould be adjusted to be higher at those times, increasing the likelihoodthat the pattern classifier will recognize that noise pattern. Thisrepetition interval may be determined in the noise characterizationstage and stored in a database 578, or may be loaded from a pre-storeddatabase (local, networked, cloud storage).

Patterns in the detection-band imaging waveform that are identified instep 570 as being associated with one or more noise classes are thenemployed to generate noise corrections to the in-band imaging waveform(e.g. the in-band imaging array). These corrections may be generatedbased on finding a correlated in-band pattern in step 575, where matchedsets of features of detection-band noise-characterization waveformpatterns, in-band noise-characterization waveform patterns and noiseclass labels are stored in a searchable database or other classificationscheme.

In the example method illustrated in FIG. 7B, an in-band noisecorrection is generated on a per-window basis, and subtracted from thein-band imaging waveform at 525, on a per-window basis, optionally aftera delay and/or amplitude adjustment and/or shape adjustment 510 thattemporally aligns the in-band noise pattern retrieved from the database575 or other classification scheme with the in-band waveform.

In one example implementation, during a noise characterization stage (inthe absence of receiving imaging energy), the detection-bandnoise-characterization array is first processed to extract one or morefeatures which are then stored. The temporal intervals at which a givennoise pattern is detected may also be determined at this stage andstored in 578. The corresponding correlated temporal pattern in thein-band noise-characterization array is also stored in 575 (for example,in a look-up-table).

According to the present example, during the imaging stage when noiseremoval is to be implemented, the detection-band imaging arrays for oneor more detection-band imaging waveforms predominantly contain noise,and are processed via the same feature extraction process. A weightvector, which assigns weights (or a priori probabilities) for each classof noise patterns, may optionally be obtained. The repetition frequencyof each pattern may be loaded from the database 578 created in a noisecharacterization stage or from a pre-stored database. The weight foreach class may be adjusted dynamically so that it is dependent on thatpattern's repetition frequency, the time instance when that pattern waspreviously detected, and the certainty with which that pattern waspreviously detected. Features extracted from the detection-band imagingwaveform, and optionally class weights, are again fed to the trainedpredictive model (trained in the noise-characterization stage), whichmay identify a noise pattern in the detection-band imaging waveform andassign it a class. A corresponding and correlated in-band noise patternfor the noise class is then obtained from the database in 575, where,for example, in-band noise waveform patterns and features ofdetection-band waveform patterns for each noise class may be stored induring the noise characterization stage (e.g. in-band temporal waveformsstored in the look up table). The in-band noise waveform patternextracted from the class comparison could be, for example, an average ofall co-incidental in-band noise patterns for the current noise class, orthe in-band noise pattern whose co-incidental detection-band patternfeatures are closest to the features of the current detection-bandimaging array, for example, determined through a nearest-neighbourcalculation. This co-incidental pattern is then subtracted from theinput after amplitude and delay adjustment to obtain a noise reducedin-band imaging waveform.

FIG. 7A and FIG. 7B show an example implementation in which a singledetection-band waveform (including energy residing, at least in part,beyond the imaging band) is generated by a single detection band passfilter 410 from which input to pattern recognizer 570 is derived.Alternatively, multiple detection-band waveforms may be generated bymultiple detection band pass filters, of which at least onedetection-band waveform is out-of-band.

In addition to the at least one detection-band waveform that carriesout-of-band noise, one or more detection-band waveforms may carry noisewithin all or a portion of the imaging band (i.e. within-bandnoise-detection waveforms). Such within-band data may be useful for thepattern recognizer 570 to confirm that the noise predicted by theout-of-band noise-detection waveforms in fact exists in the imaging band(either during the noise characterization stage, or during imaging).

For example, within-band noise-detection imaging waveforms may also beemployed by the pattern recognizer to identify a noise source. Forexample, if the energy in some sub-bands of the imaging band issubstantially different relative to one or more other sub-bands of theimaging band, or relative to the net energy within the imaging band,then a noise source associated with the imaging sub-band may beidentified. For example, a peak filter centered at 8 MHz may be used toobtain a within-band noise detection waveform within an imaging bandranging from 7 to 13 MHz, and a detection band-pass filter with a passband of 15 to 25 MHz may be used to obtain an out-of-bandnoise-detection waveform. If the 8 MHz within-band noise-detectionwaveform detects an increase in energy relative to the energy in theout-of-band noise-detection waveform in the 15-25 MHz range, the patternrecognizer may be able to adjust its weights to preferentially detect aspecific noise source (i.e. noise class). The system may better select acorrelated in-band noise pattern from the database at 573 to remove that8 MHz peak than if it solely relied on information that was out-of-bandto the imaging band.

It will be understood that although the preceding example embodimentswere disclosed within the context of detecting temporal patterns in rawor envelope-processed signals, the preceding algorithm may alternativelybe adapted for implementation using image data. For example, image data(e.g. B-mode image data) may be processed to determine spatial noisepatterns instead of processing time domain (e.g. RF orenvelope-detected) signals. These alternative embodiments are shown witha dotted path in FIG. 7B, where decimation (220 and 221) and B-modeimage line generation (230 and 231) is performed prior to 570 and 575.

Alternatively, when processing images in spatial domain, 2D imagingwindows may be used to detect spatial patterns, such as for B-mode imagedata. For B-mode data, texture features may be extracted in the spatialdomain (e.g. gray level co-occurrence matrices), frequency domain (e.g.Fourier spectrum measurements), or spatial frequency domain (e.g. energyof 2D wavelet coefficients).

Referring now to FIG. 7D, an alternative example embodiment is shown inwhich a reference noise-detection waveform is employed, instead of thedetection-band imaging waveform of FIG. 7B, when performing patternrecognition during imaging. Similarly, referring now to FIG. 7C, areference noise-characterization waveform may be employed during theinitial pattern recognition stage that is performed in the absence ofimaging signal. The algorithms or schema described above, with referenceto FIG. 7A and FIG. 7B, may thus be adapted to the present exampleembodiment by replacing the detection-band noise-characterizationwaveform (and associated array measures) with the referencenoise-characterization waveform (shown in FIG. 7C), and replacing thedetection-band imaging waveform (and associated array measures) with thereference noise-detection waveform (shown in FIG. 7D).

Embodiment 5: Noise Reduction Based on Relative Energy Measures

In the present example embodiment, noise reduction is performed byselectively attenuating a windowed portion of an in-band imagingwaveform, based on criteria that are assessed according to measurementsfrom one or more detection-band imaging waveforms, at least one of whichis an out-of-band imaging waveform. Attenuating may refer, for example,to subtracting a derived subtrahend value from the envelope of thewindowed in-band imaging waveform, and/or multiplying the windowedin-band imaging waveform or its envelope with an attenuation factor,where the subtrahend value and/or the attenuating factor are determinedfrom measurements on the noise-detection imaging waveforms or referencenoise-detection waveforms.

According to a first stage of the present example method, in a noisecharacterization stage, energy is detected within both an imaging band(in-band) and a noise-detection band in the absence of imaging energy(such as during a non-imaging noise characterization stage), therebyobtaining correlated measurements of in-band noise and detection-bandnoise. At least one noise-detection band is out-of-band. Samples fromthe in-band noise-characterization waveform and the detection-bandnoise-characterization waveform are recorded as pairs of arrays, wherean array pair refers to a sampled in-band waveform and a secondcorresponding sampled detection-band waveform recorded at the same time.

Optionally, the noise characterization stage may include an additionalstage, referred to as a baseline noise characterization stage, when itis either known or anticipated that the imaging transducer receivecircuit is not receiving imaging energy and not receiving noise energy.As shown in FIG. 6A, an in-band baseline noise characterization arraymay be obtained by applying an imaging band pass filter 200 to an inputwaveform detected from an imaging transducer receive channel 13 in theabsence of receiving imaging energy and in the absence of receivingnoise energy, and optionally detecting an envelope of the filteredwaveform at 210. A detection-band baseline noise-characterization arraymay be obtained by applying a detection-band filter 410 to an inputwaveform from an imaging transducer receive channel 13, and optionallydetecting an envelope of the filtered waveform at 411. The in-bandbaseline noise characterization array and the detection-band baselinenoise-characterization array, measured in the absence of imaging energyand absence of noise energy, are denoted as Gi and Gn, respectively. Agiven array pair may optionally be segmented according to a plurality oftime windows, as illustrated in FIG. 6A at 565 and 566, to obtainwindowed array pairs, denoted as Gi_(w) and Gn_(w). The windows may besliding windows, with optional overlap between adjacent windows. One ormore noise measurements may be calculated from the per-window energymeasurements in a baseline noise characterization stage. For example,the maximum power within a windowed in-band baselinenoise-characterization array may be denoted as Ti. Similarly, themaximum power within a windowed out-of-band baseline noisecharacterization array may be denoted as Tn.

The noise characterization stage includes a stage when the imagingtransducer receive circuit is not receiving imaging energy but isanticipated to receive noise energy. Referring again to FIG. 6A, anin-band noise characterization array 407 may be obtained by applying animaging band pass filter 200 to an input waveform detected from animaging transducer receive channel 13 in the absence of receivingimaging energy, and optionally detecting an envelope of the filteredwaveform at 210. A detection-band noise-characterization array may beobtained by applying a noise-detection band filter 410 to an inputwaveform from an imaging transducer receive channel 13, and optionallydetecting an envelope of the filtered waveform at 411. The in-band noisecharacterization array 407, and the -detection-band noisecharacterization array 408, measured in the absence of imaging energy,are denoted as Ci and Cn, respectively (as shown in FIG. 6A).

A given array pair may optionally be segmented according to a pluralityof time windows, as illustrated in FIG. 6A at 565 and 566, to obtainwindowed array pairs, denoted as Ci_(w) and Cn_(w). The windows may besliding windows, with optional overlap between adjacent windows. Inanother example, the windows may be centered around a peak amplitude ofthe noise waveforms in one or more detection bands and/or in the imagingband. In yet another example, the time windows may be time-locked tonoise onsets determined when the amplitude of noise in one or moredetection bands and/or the imaging band exceeds predefined thresholds.For example, the threshold may be proportional to parameters Tn and/orTi obtained during a baseline noise characterization stage.

The in-band and detection-band noise-characterization array pairs Ci_(w)and Cn_(w) are processed to obtain one or more measures associated withthe energy in the imaging band and a noise-detection band for each timewindow, in order to characterize the relative intensity of the noisewithin the two bands. For example, as shown in FIG. 6A, for each pair ofwindows Ci_(w) and Cn_(w) the power in the imaging band and the power inthe noise-detection-band may be calculated at 570 and 572.

Noise-characterizing measurements may optionally be calculated only fromselect windows, where selection criteria may be assessed according toin-band energy measurements, and optionally detection-band energymeasurements. For example, only windows for which the in-band powerexceeds a predefined threshold may be selected for obtainingnoise-characterizing measurements, as shown in FIG. 6B. The thresholdmay be proportional to Ti obtained during a baseline noisecharacterization stage. In another example, only windows for which theout-of-band power exceeds a predefined threshold may be selected forobtaining noise-characterizing measurements. The threshold may bederived from Tn obtained during a baseline noise characterization stage.

It is to be understood that, in the proceeding examples, maximum andminimum values may refer to either upper and lower percentiles, or truemaximum and minimum values. For example, 98^(th) and 2^(nd) percentilesvalues may be used instead of the maximum and minimum values. Otherstatistical thresholds, such as the 95^(th) and 5^(th) percentiles,90^(th) and 10^(th) percentiles and 80^(th) and 20^(th) percentiles orothers may be used to represent the maximum and minimum values forcharacterization purposes.

One or more noise measurements may be calculated from the per-windowenergy measurements in a noise characterization stage, and may be usedto define a relationship between power in the imaging band and power inthe detection band in the presence of a noise source. For example, oneor more pairs of in-band and detection-band power values may be selectedas inflection points for generating a piece-wise linear function todefine the relationship between power in the imaging band and power inthe detection band in the presence of a noise source, as shown in FIG.6B.

In one example, a piece-wise linear function defining the relationshipbetween the in-band power and the detection-band power in the presenceof a noise source may be generated based on maximum and minimum powervalues obtained in a noise characterization stage, as shown in FIG. 6B.Minimum and maximum in-band power values from a noise characterizationstage may be evaluated (e.g. as absolute maximum/minimum values or usingstatistical measures) and denoted as Pi_(min) and Pi_(max),respectively. In one example implementation, a set of windows whosein-band power falls within a preselected range relative to Pi_(min)(e.g. within a percentile range) may be identified, and, from among theidentified set of windows, the minimum detection-band power may beselected as Pn_(min). Similarly, a set of windows whose in-band powerfalls within a preselected range relative to Pi_(max) may be identified,and, from among the identified set of windows, the minimumdetection-band power may be selected as Pn_(max). Power pairs (Pn_(min),Pi_(min)) and (Pn_(max), Pi_(max)) may be used for the fitting of afunction defining an estimated relationship between in-band anddetection-band power. It is to be understood that the exampleimplementation is just one non-limiting example of selecting values ofin-band and detection-band powers to provide suitable fitting pointsand/or a functional relationship between in-band and detection-bandpower, and other methods may be alternatively employed.

Optionally, the ratio of the power in the in-band noise-characterizationwaveform to the power in the detection-band noise-characterizationwaveform may be calculated on a per-window basis, and the maximum ratioR^(off) across a plurality of windows (an example use of this quantityis described below when determining whether or not to apply a noisecorrection during imaging) may be obtained.

One or more relationships f(Pn) between the power in the in-bandnoise-characterization waveform (Pi) and power in the detection-bandnoise-characterization waveform (Pn) may be obtained. For example, asshown in FIG. 6B, f(Pn) may be a piece-wise linear function whose slope,intercept and/or inflection points are defined by the points (Pn_(min),Pi_(min)) and (Pn_(max), Pi_(max)) calculated in a noisecharacterization stage. In another example f(Pn) may be a non-linearpolynomial, or a combination of one or more linear or non-linearpolynomials. In yet another example, f( ) may be a set of values definedfor one or more ranges of Pn values. For example, f(Pn) may be assigneda value Pi_(a) for a₁≤Pn<a₂, f(Pn) may be assigned a value Pi_(b) forb₁≤Pn<b₂ and so forth, where [a₁, a₂] and [b₁, b₂], and so forth, arenon-overlapping intervals of Pn. A set of windows of the detection-bandnoise characterization array whose power lies between a₁ and a₂ may beidentified, and the associated windows of the in-band noisecharacterization array may be employed to determine a value for Pi_(a)for the interval [a₁, a₂]. For example, Pi_(a) may be a representativepower value (such as maximum, mean, median or some other measure)calculated from in-band power measurements of all windows whosedetection-band power, Pn, lies within the range [a₁, a₂].

Noise measurements obtained in a noise characterization stage may beemployed to perform noise reduction of the in-band imaging waveformobtained during imaging, as shown in FIG. 6C and FIG. 6D. The inputwaveforms, filtered as shown at 200 and 410 to provide the in-bandimaging waveform 407 and the detection-band imaging waveform 408,optionally after envelope detection at 210 and 411. The waveforms may besampled to obtain in-band imaging arrays and detection-band imagingarrays.

In the present example embodiments, the term “in-band imaging array” isemployed to refer to a sampled in-band imaging waveform. The term“detection-band imaging array” is employed to refer to a sampleddetection-band imaging waveform. A set of arrays may be recorded, whereeach array may be respectively associated with a given scan line. Forexample, a first in-band imaging array may be associated with a firstscan line, a second in-band imaging array may be associated with asecond scan line, and so forth. The in-band and detection-band imagingarrays are denoted as Qi_(θ), and Qn_(θ), respectively, where θ is anindex identifying a given period of acquisition, such as onecorresponding to a scan line.

The arrays may be windowed, as shown at 565 and 566 in FIG. 6C, such asusing the same window properties as those employed to window the noisecharacterization arrays in the characterization stage. The in-bandimaging arrays and detection-band imaging arrays, temporally segmentedaccording to the windows, are denoted as Qi_(θ,w) and Qn_(θ,w),respectively, where the subscript w is an integer denoting the windownumber. For example, Qi_(1,10) refers to the 10^(th) window portion ofthe in-band imaging array corresponding to the 1^(st) scan line. Foreach windowed portion of the in-band imaging array and thedetection-band imaging array, power or another suitable energymeasurement may be calculated, as shown at 570 and 572. In-band anddetection-band power values, on a per-window basis, are calculated asP(Qi_(θ,w)) and P(Qn_(θ,w)), respectively. These energy measurements maythen be used for suppressing noise based on measurements obtained in anoise characterization stage.

In one example implementation, shown in FIG. 6C, noise may be suppressedby subtracting a power value from the envelope of the in-band imagingarray Qi_(θ,w) at 525. Noise-detection band power P(Qn_(θ,w)), may beused to estimate a noise energy, {circumflex over (P)}i_(N θ,w), withinthe in-band imaging array window Qi_(θ,w) based on the function f( )obtained in a noise characterization stage. For example, the noiseenergy {circumflex over (P)}i_(N θ,w) within Qi_(θ,w) may be estimatedat 575 as {circumflex over (P)}i_(N θ,w)=ƒ(P(Qn_(θ,w))). The estimatedin-band noise {circumflex over (P)}i_(N θ,w) may optionally be scaled bymultiplication with a scaling factor β, where 0≤β≤1. The scaledestimated noise β{circumflex over (P)}i_(N,w) may optionally be clampedbelow an upper limit, such as, but not limited to 0.8×P(Qi_(θ,w)),0.9×P(Qi_(θ,w)) or 1×P(Qi_(θ,w)) to obtain a subtrahend value. Thesubtrahend value may be subtracted from the envelope of Qi_(θ,w) at 525to obtain a noise reduced waveform envelope.

In another example implementation shown in FIG. 6D, noise may besuppressed by multiplying the elements of the in-band imaging arrayQi_(θ,w) by an attenuating factor at 526. During imaging, the in-bandpower in an imaging window Qi_(θ,w) may be calculated as P(Qi_(θ,w)).Noise-detection band power P(Qn_(θ,w)), may be used to estimate a noiseenergy, {circumflex over (P)}i_(N θ,w), within the in-band imagingwindow Qi_(θ,w) based on the function f( ) obtained in a noisecharacterization stage. For example, the noise energy, {circumflex over(P)}i_(N θ,w) within Qi_(θ,w) may be estimated at 575 as {circumflexover (P)}i_(N θ,w)=ƒ(P(Qn_(θ,w))). The estimated in-band noise{circumflex over (P)}i_(N θ,w) may optionally be scaled bymultiplication with a scaling factor β, where 0≤β≤1. An attenuatingfactor may be selected to be proportional to[P(Qi_(θ,w))−βPi_(N θ,w)]/P(Qi_(θ,w)).

In some example implementations, scaling factor β may be selected to liebetween zero and unity. In ultrasound, where attenuation of ultrasoundenergy causes the imaging energy to be reduced over time, thedetermination of β may be dependent on the depth of the window withinthe waveform (thus corresponding to a depth within the imaged tissue).The parameter β may optionally be user-controlled. The attenuatingfactor may optionally be clamped below an upper limit, such as, but notlimited to, unity, 0.95, 0.9, or 0.8. The attenuating factor mayadditionally or alternatively be clamped above a lower limit, such as,but not limited to, 0, 0.01, 0.05, or 0.1. The attenuating factor may bemultiplied with the array Qi_(θ,w) at 526 or its envelope to obtain anoise reduced array.

Pattern Recognition for Class-Specific Noise Reduction

During a noise characterization stage (see, for example, FIG. 6A), thesystem may optionally be configured to group array pairs Cn_(w) andCi_(w) into one or more categories, referred to as classes. Referringback to the pattern recognizer described in step 570 of FIG. 7A, one ormore detection-band noise-characterization waveforms may be processed bya pattern recognizer in order to identify one or more classes of noisepatterns and to assign a class to temporal windows associated with anidentified noise pattern. A set of windows of the detection-bandnoise-characterization waveform belonging to a class k may be selectedand denoted as {w_k}. In-band and out-of-band power measures for windowswithin set {w_k} may be used to derive a functional relationship f_(k)() specific to the class k, using methods similar to those described inthe preceding paragraphs.

The system may be configured to employ a pattern recognizer to identifynoise patterns when the imaging transducer circuit is receiving imagingenergy. Using methods similar to those described for step 570 of FIG.7B, one or more detection-band imaging waveforms may be processed by apattern recognizer in order to identify one or more classes of noisepatterns and to assign a class to temporal windows associated with anidentified noise pattern. The estimated in-band noise power may bederived from a functional relationship f( ) specific to the identifiedclass. For example, for windows belonging to class 1, function f₁( ) maybe used to derive an estimated in-band noise (i.e. {circumflex over(P)}i_(N θ,w)=ƒ₁ (P(Qn_(θ,w))). Similarly, for windows belonging toclass 2 function f₂( ) may be used to derive an estimated in-band noise,and so forth, where f₁( ), f₂( ), f₃( ), etc. are obtained in a noisecharacterization stage. As described previously, noise in a given windowQi_(θ,w) of the in-band imaging waveform may be reduced by subtracting asubtrahend value from the envelope of Qi_(θ,w), or by multiplyingQi_(θ,w) with an attenuation factor, where the subtrahend value or theattenuation factor are derived from {circumflex over (P)}i_(N θ,w). As afurther specific example, noise classified by the pattern recognizer asclass 1 may originate from an electroanatomic mapping system and noiseclassified as class 2 may originate from an ablation energy generator.Therefore, f₁( ) could be used to estimate the in-band noise generatedby the electroanatomic mapping system based on the power in thenoise-detection band when the noise is recognized by the system to comefrom the mapping system, and f₂( ) could be used to estimate the in-bandnoise generated by the ablation generator based on the power in thenoise-detection band when the noise is recognized by the system to comefrom the ablation generator.

In another example, systems that emanate noise may be monitored todetermine which functional relationship to use for noise estimation andsuppression. For example, the controls of an ablation generator may bemonitored so that a binary gating signal is enabled when the ablationgenerator is actively generating energy (and associated noise). Thisgating signal may be used to determine the time periods when functionf₂( ) is to be used for in-band noise estimation.

Selectively Performing Noise Reduction

Energy measurements may optionally be employed to estimate whether ornot the in-band imaging array is likely to exhibit a low signal-to-noiseratio. In other words, energy measurements may be employed to classifywindows as to whether or not to apply a noise reduction correction (viasubtraction or multiplication with an attenuating factor).

In one example implementation, the decision about whether or not toapply a noise reduction correction for a given window, w, can be madebased on the power detected in the noise-detection band. Noisecorrection is applied for Qi_(θ,w) if P(Qn_(θ,w)) exceeds a predefinedthreshold. The threshold may be obtained in a noise characterizationstage.

In other instances the decision about whether or not to apply a noisereduction correction for a given window can be made based on ratio ofthe power in the imaging band relative to the power in thenoise-detection band. For example, in a noise-characterization stage,for each window of the pairs of in-band noise-characterization arraysand detection-band noise-characterization arrays, the ratio of the powerin the imaging band to the power in the detection band may be obtained.The representative maximum ratio across all windows, denoted R^(Off),may be used as a threshold to decide whether or not to apply noisereduction correction when the imaging transducer receive circuit isreceiving imaging energy, as described below.

The decision about whether or not to apply a noise reduction correctionfor a given window, w, of an in-band imaging waveform can be made basedon ratio of the power in the imaging band relative to the power in thenoise-detection band, denoted as R^(On) _(w). In one exampleimplementation, R^(On) _(w) is compared to γR^(Off), where γ is arelaxation parameter and R^(Off) is the representative maximum ratiocalculated in a noise characterization stage. If R^(On) _(w) isdetermined to be greater than γR^(Off), then it is estimated that thesignal-to-noise ratio of the in-band signal is sufficiently high and anoise reducing correction is not applied. Conversely, if R^(On) _(w) isdetermined to be less than or equal to γR^(Off), then it is estimatedthat the signal-to-noise is sufficiently low to warrant the applicationof a noise reduction correction.

The value of γ may be employed to adjust the sensitivity tosignal-to-noise, and may be used, in some cases, as an adjustment factorfor cases in which weak portions of the signal would otherwise besuppressed. Lowering the value of γ will lower the threshold forapplying noise reduction, thereby reducing the number of windows thatundergo noise reduction and allowing more imaging energy (and noise) topersist in the final output.

In some example implementations, γ may be selected to lie between zeroand unity. In ultrasound, where attenuation of ultrasound energy causesthe imaging energy to be reduced over time, the determination of γ maybe dependent on the depth of the window within the waveform (thuscorresponding to a depth within the imaged tissue). The parameter γ mayoptionally be user-controlled. For example, in cases in which tissue orother structural aspects of the image are perceived to be unnecessarilyor overly attenuated, the user can reduce the value of this parameter inorder to lessen the effect of noise reduction.

For windows that are identified for noise reduction, any suitable noisereduction or suppression method may be employed to reduce noise of thein-band imaging array within the window. It will be understood that awide variety of noise reduction corrections may be applied, such as, butnot limited to, corrections involving subtraction as shown in FIG. 6Cand/or multiplication with an attenuation factor as shown in FIG. 6D.

In some cases, the noise suppression can cause erroneous noise reductionon a per-window basis. For example, some windows that contain a smallamount of imaging energy may inadvertently undergo noise reduction basedon an erroneous determination of a low signal-to-noise ratio in a window(i.e. false window classification). This can result in some small image“holes” in a surrounding homogenous signal region of an image, orresidual noise pixels in a surrounding low-noise region of an image. Inone example embodiment, the status of adjacent windows (i.e. windows inthe spatial neighborhood) may be employed to determine whether or not awindow that is identified as being suitable for noise reduction shouldin fact undergo such a process. If a given window is identified as beingsuitable for noise reduction as per the aforementioned methods, thenadjacent windows in one or more adjacent arrays (i.e. arrayscorresponding to adjacent scan lines) may be employed to assess whetheror not noise reduction by amplitude attenuation of the given windowshould be performed.

For example, if, for a given scan line, a given window is identified asnot being suitable for noise reduction, yet one or more adjacent windowsare identified as being suitable for noise reduction, the window may beflagged as being likely misclassified. The status of the given windowmay be overridden and the given window may instead by identified asbeing suitable for noise reduction, such that noise reduction is appliedto the given window. Conversely, if a given window is identified asbeing suitable for noise reduction, yet adjacent windows within adjacentarrays are identified as not being suitable for noise reduction, thenthe initial determination of the status of the given window may beoverridden such that the given window is instead identified as not beingsuitable for noise reduction and is flagged as a window that is likelymisclassified. Samples of misclassified windows may be replaced bysamples of one or more non-noisy neighboring windows (i.e. replaced byor interpolated from samples of adjacent windows identified as not beingsuitable for noise reduction as per the aforementioned methods)optionally after performing delay and amplitude adjustments.

An example implementation of this method is illustrated in FIG. 6E,where adjacent windows in adjacent arrays are interrogated to determinewhether or not the classification of the current window is consistentwith its surrounding windows in adjacent arrays. Since two windows(array 2, windows 4 and 5) classified as containing primarily noise(marked “N”) are surrounded by windows (marked “S”) classified as havinga sufficiently high signal power to avoid the need for noise reduction,the two “N” windows may be replaced with samples from neighboring “S”windows (for example, by copying or by interpolating with amplitudeadjustment and/or shape adjustment), and noise reduction by noiseestimation will not be performed on these windows, as shown in FIG. 6F.Although the present example implementation employs two adjacent windowson either side of the array when confirming the status of a givenwindow, other embodiments may employ any number of adjacent windows.

One or more temporally adjacent windows before and after a given windowmay also be employed when assessing whether the classification of agiven window should be altered. For example, in FIG. 6E, window 4 ofarray 5, initially marked “S”, may be reclassified as “N”, shown in FIG.6F, since preceding and proceeding windows of the array are marked “N”.

Although the present example embodiment involves the processing ofsignals prior to image processing, the present example embodiment may beadapted to process image data as opposed to the processing oftime-domain signals. For example, a plurality of in-band anddetection-band imaging arrays, representing a plurality of adjacent scanlines, may be acquired and post-processed to obtain in-band image anddetection-band image frames. The in-band and detection-band image pixelsare denoted as Bi_(θ,d) and Bn_(θ,d), respectively, where θ is denotes ascan line and d is the depth. The detection-band image may be used toevaluate attenuation (i.e. via subtraction or multiplication) values foreach pixel in the in-band image. These attenuation values may beobtained from a corresponding pixel in a detection-band image on aper-pixel basis (i.e. the value at Bn_(θ,d) may be used to attenuate thepixel intensity at Bi_(θ,d)) or by processing a region-of-interest inthe detection-band image that would correspond to a local spatialneighborhood of an in-band image pixel (e.g. the values of a 3×3neighborhood (e.g. in polar or Cartesian co-ordinates) around Bn_(θ,d)may be used to attenuate the pixel intensity at Bi_(θ,d)).

Referring now to FIGS. 6G, 6H and 6J, alternative example embodimentsare shown in which a reference receive channel is used when performingnoise characterization (FIG. 6G) and for the determination of a suitablesubtrahend value (FIG. 6H) or attenuation factor (FIG. 6J) for noisesuppression during imaging. In FIG. 6G, a reference noisecharacterization waveform 409 is employed, instead of the detection-bandnoise characterization waveform of FIG. 6A, when performing noisecharacterization. The reference channel filter could be an imaging bandpass filter. Alternatively, if the noise estimation benefits from inputof out-of-band noise, then the reference channel filter may be differentfrom an imaging band pass filter. In FIG. 6H, a referencenoise-detection waveform 439 is employed, instead of the detection-bandimaging waveform of FIG. 6B, to determine subtrahend values and apply anoise reduction by subtraction. Similarly, in FIG. 6I, a referencenoise-detection waveform 439 is employed, instead of the detection-bandimaging waveform of FIG. 6B, to determine and apply an attenuationfactor for noise reduction. The methods described above, with referenceto FIGS. 6A to 6F, may thus be adapted to the present example embodimentby replacing the detection-band noise-characterization waveform 408 (andassociated power measures) with the reference noise-characterizationwaveform 409 shown in FIG. 6G, and replacing the detection-band imagingwaveform 438 (and associated array and power measures) with thereference noise-detection waveform 439 shown in FIG. 6H and FIG. 6I.

Embodiment 6: Noise Reduction of Pseudo-Periodic Noise Sources

In the present example embodiment, noise reduction is performed byestimating and subtracting in-band noise, where the noise is expected tooriginate from a pseudo-periodic noise source or a pseudo-periodicsequence of noise sources. The in-band noise is estimated based onmeasurements made during a noise characterization stage when an imagingtransducer receive circuit is not receiving imaging energy.

According to a first stage of the present example method, waveforms aredetected within both an imaging band and a noise-detection band in theabsence of imaging energy (e.g. when an ultrasound transducer is notreceiving imaging energy) and sampled, thereby obtaining a pair ofco-incidental in-band and detection-band (out-of-band) noisecharacterization arrays.

As shown in FIG. 8A, an in-band noise-characterization waveform 590 maybe obtained by applying an imaging band pass filter 200 to an inputwaveform detected from an imaging transducer receive channel 13, andoptionally detecting an envelope 210 of the filtered data. Adetection-band noise-characterization waveform 595 may be obtained byapplying a noise-detection bandpass filter 410 to an input waveform froman imaging transducer receive channel (where at least onenoise-detection band comprises signal from outside of the imaging band),and optionally detecting an envelope 411 of the filtered data. Thein-band noise-characterization waveforms and detection-band noisecharacterization waveforms, measured in the absence of imaging energy,may be sampled to obtain in-band noise-characterization arrays anddetection-band noise characterization arrays, denoted as Ci and Cn,respectively. Ci (and Cn) should capture one or more periods of aperiodic noise source.

Having obtained the in-band and detection-band noise characterizationarrays in a noise characterization stage, the correlation between thedetection-band characterization array and a detection-band imaging arraymay be used to estimate adjustment parameters for subtracting thein-band characterization array from the in-band imaging array.

As shown in FIG. 8B, when performing imaging, the input waveforms froman imaging transducer receive circuit 13 are filtered as shown at 200and 410 to provide in-band imaging waveforms and detection-band imagingwaveforms. These waveforms may be sampled (before or after performingenvelope detection) to obtain an in-band imaging array 537 and adetection-band imaging array 538. The in-band and detection-band imagingarrays are denoted as Qi and Qn, respectively.

According to the present method, each in-band imaging array is processedfor noise reduction using the in-band noise-characterization array Ci tosubtract noise from the in-band imaging array Qi. However, in order toperform noise reduction via subtraction, the in-band noisecharacterization array Ci should be temporally aligned with the in-bandimaging array Qi such that noise is co-incidental. Such alignment ispossible in the case of a periodic noise source that generates noise inthe imaging band that is correlated with noise in anoise-detection-band.

The temporal alignment may be achieved, for example, by segmenting thein-band and -detection-band imaging arrays Qi and Qn into a plurality oftime windows at 565 and 566 (as described in the preceding exampleembodiments). The windows should preferably be long enough to captureone or more periods of the periodic noise source. The imaging arrays,temporally segmented according to the windows, are denoted as Qi_(w) andQn_(w), where the subscript w is an integer denoting the window number.

In one example embodiment, temporal alignment may be achieved on aper-window basis. In the present example implementation, the temporalalignment may be achieved by calculating, within each window, thecross-correlation between the detection-band noise characterizationarray Cn and the detection-band imaging array Qn_(w), and selecting therelative time delay τ corresponding to the maximum cross-correlation, asshown at 580. Due to the co-incidental relation between the noise in theimaging band and noise in the noise-detection band, this time delay tcan also be applied to align the in-band noise characterization array Cirelative to the in-band imaging array Qi_(w), on a per-window basis asshown at 510. A scaling factor may also be applied to the alignedin-band noise characterization array.

A windowed portion of the aligned in-band noise characterization arraywhich is denoted by Ci_(w), is then subtracted from the in-band imagingarray Qi_(w), resulting in a noise-reduced in-band imaging array, Qi_(w)optionally after having taken the absolute value post-subtraction orapplying a floor function in order to eliminate negative values. Thisprocess may then be repeated for each additional window for which noisereduction is desired.

In one example implementation, in which adjacent windows overlap, ascaling factor may be applied when subtracting the aligned windowedsegment of the in-band noise characterization array Ci_(w) from thein-band imaging array Qi_(w). For example, the subtraction (and optionalmodulus) may be calculated according to: Qi_(w)=Qi_(w)−αCi_(w), where αis the scaling factor to account for windowing, and where α=1−β, where βis the overlap factor. For example, with β=0.75, the scaling factorwould be α=0.25. It will be understood that the present implementationis provided to illustrate an example method of scaling the subtractedcomponent, and that other functional forms may alternatively beemployed.

Referring now to FIGS. 8C and 8D, an alternative example embodiment isshown in which a reference receive channel is used when performing noisecharacterization and for the determination of a suitable amplitudeadjustment for noise suppression during imaging. In FIG. 8C, a referencenoise characterization waveform 596 is employed, instead of thedetection-band noise-characterization waveform 595 of FIG. 8A, whenperforming noise characterization. Similarly, in FIG. 8D, a filteredreference noise-detection waveform 439 is employed, instead of thedetection-band imaging waveform of FIG. 8B, to determine and apply theamplitude adjustment. The methods described above, with reference toFIGS. 8A and 8B, may thus be adapted to the present example embodimentby replacing the detection-band noise characterization waveform 408 withthe reference noise-characterization waveform 409 shown in FIG. 8C, andreplacing the detection-band imaging waveform 438 (and associatedarrays) with the reference noise-detection waveform 439 shown in FIG.8D.

Embodiment 7: Noise Reduction Using a Plurality of Scans by ChangingScan Rate

The system may be configured such that a set of two or more in-bandimaging arrays associated with imaging energy from the same scan line orwith scan lines with substantial spatial overlap are obtained. Theimaging energy within the set of in-band imaging arrays will haveredundant temporal/depth dependency. Averaging (or performing some otherstatistical processing, such as evaluating the minimum value) the set ofredundant in-band imaging arrays may suppress noise if the noise itselfis not time-locked to the trigger that prompts the imaging transducerreceive circuit to start receiving imaging energy for each scan line.For example, if the pulse repetition frequency of the voltage pulse thatexcites an imaging ultrasound transducer is 200 us, a periodic noisethat repeats every 2 us and will always have a component at 0 us, 2 us,4 us and so forth for each in-band imaging array. However, if the pulserepetition frequency is adjusted to 199 us, a first imaging array willhave noise components at 0 us, 2 us, 4 us and so forth, and a secondimaging array will have noise components at 1 us, 3 us, 5 us and soforth. Noise may be suppressed by averaging two successive redundantin-band imaging arrays, optionally after performing envelope detection.

In this example embodiment, noise in a detection-band waveform(out-of-band) may be used to determine the period of one or more in-bandnoise sources. The system may be prompted to adjust its scan rate sothat imaging scan period is not an integer multiple of the period of anoise source. For example, an auto-correlation function may be used todetect periodicity in a detection-band imaging waveform while an imagingtransducer receive circuit is receiving imaging energy. Alternatively,the period of one or more noise sources may be determined from adetection-band noise-characterization waveform or referencenoise-characterization waveform in a noise characterization stage whenthe imaging transducer is not receiving imaging energy, or may be loadedfrom a pre-stored database. The system may then be prompted to adjustits scan rate so that imaging scan period is not an integer multiple ofthe period of a noise source.

Once the optimal scan rate is determined and the scan rate is adjusted,in-band imaging waveforms obtained from a plurality of scan lines aresampled after performing envelope detection to obtain a set of in-bandimaging arrays. An in-band imaging array is denoted as Qi_(θ), where θis a scan line. A sample from an in-band imaging array is denoted asQi_(θ)[k], where k=1 . . . K is the sample index and K is the number ofsamples in the array. The system may be configured to suppress noise byaveraging (or performing another statistical measurement, such as takingthe minimum) across a plurality of arrays associated with adjacent scanlines or scan lines with significant spatial overlap. For example, ifthe system is configured to group 3 in-band imaging arrays, a sampleQi_(θ)[k] may be replaced by the average of [Qi_(θ−1)[k], Qi_(θ)[k],Qi_(θ+1)[k]]. Optionally, a sample Qi_(θ)[k] may be selectively retainedafter performing some other numerical analysis on the set [Qi_(θ−1)[k],Qi_(θ)[k], Qi_(θ+1)[k]] and determining whether the sample warrantsnoise reduction. For example, if for [Qi_(θ−1)[k], Qi_(θ)[k],Qi_(θ+1)[k]] the minimum value is greater than half the maximum value,the range of sample values may not be large enough to warrant noisereduction by averaging and that the sample is therefore left unchanged.

Such a scheme may also be useful in MRI imaging in the presence of aperiodic noise source, by ensuring that RF excitation pulses areinitiated at times that do not correlate with the timing or periodicityof noise sources in the local environment.

Additional Feature: Selectively Choosing Samples to Undergo NoiseReduction when Using a Plurality of Scans

In the present example embodiment, the scan rate is adjusted asdescribed above, and noise reduction is performed by selectivelyreplacing portions of an in-band imaging array based on statisticalmeasurements (such as average or minimum) from arrays from a pluralityof adjacent scan lines, where there is sufficient overlap in the scanregion associated with the adjacent scan lines. Only segments of thein-band imaging array that are assessed as noisy using detection-bandmeasurements are replaced.

As shown in FIG. 8E, input waveforms, filtered as shown at 200 and 410to provide in-band waveforms and detection-band-waveforms may be sampledafter performing envelope detection to obtain in-band imaging arrays anddetection-band imaging arrays.

Data corresponding to a plurality of scan lines is recorded as sets ofarrays for each scan line. A set of arrays are obtained for each scanline, where one in-band imaging array is obtained in an imaging band foreach scan line, and at least one out-of-band imaging array is obtainedfor each scan line. The in-band and detection-band imaging arrays in apair are denoted as Qi_(θ) and Qn_(θ), respectively, where θ is a scanline. A sample from the in-band imaging array and detection-band imagingarray is denoted as Qi_(θ)[k] and Qn_(θ)[k], respectively where k=1 . .. K is the sample index and K is the number of samples in the array.

The detection-band imaging arrays are segmented into a plurality ofwindows, each containing J samples of Qn_(θ). An array segmentedaccording to windows is denoted as Qn_(θ,w), where the subscript w is aninteger denoting the window number, θ is an index denoting the scanline, and Qn_(θ,w) contains samples [Qn_(θ)[k_(w)], Qn_(θ)[k_(w+1)], . .. Qn_(θ)[k_(w+J−1)], where k_(w) is an index of the first sample in thewindow.

Each window Qn_(θ,w) in the detection band is assessed for the presenceof or absence of noise, as shown at 600. If noise (determined, forexample, by measurements of waveform energy such as peak, RMS, etc.)exceeds a threshold, the window is deemed noisy. The threshold may beselected in a noise characterization stage.

According to the present example embodiment, for each detection-bandarray window Qn_(θ,w) classified as being noisy, all the co-incidentalin-band samples (i.e. Qi_(θ)[k_(w)], Qi_(θ)[k_(w+1)], . . .Qi_(θ)[k_(w+J−1)]) may be identified as samples that are suitable fornoise reduction.

When windows overlap, a given sample Qi_(θ)[k] may be associated withmore than one window. There may be instances when the sample isassociated with both noisy and noise-free windows. In these cases, thesystem may be configured to pool noise assessments from multipleout-of-band windows before determining if the sample is suitable fornoise reduction.

Samples deemed suitable for noise reduction may be replaced at 620 usingstatistical measures (such as duplicate values, average, minimum, andthe like) from samples from arrays associated with adjacent scan lines,computed as shown at 610. For example, if sample Qi_(θ)[k] is deemedsuitable for noise reduction, and the system is configured to grouparrays from 3 scan lines, sample Qi_(θ)[k] may be replaced by theminimum of [Qi_(θ−1)[k], Qi_(θ)[k], Qi_(θ+1)[k]].

Referring now to FIG. 8F, an alternative example embodiment is shown inwhich a reference waveform employed, instead of the detection-bandimaging waveform of FIG. 8E, when performing noise reduction duringimaging. The methods described above, with reference to FIG. 8D, maythus be adapted to the present example embodiment by replacing thedetection-band imaging waveform (and associated arrays) with thefiltered reference noise-detection waveform.

Time-Domain Vs. Frequency Domain Processing

The preceding example embodiments have been disclosed within the contextof time-domain processing. However, many of the example embodimentsdisclosed herein may employ frequency-domain or time-frequency domainprocessing during one or more steps. For example, in FIGS. 6A and 6C, insteps 570 and 572, instead of using maximum power and ratio of maximumpower in the imaging band and noise-detection band, short-term FourierTransform or wavelet transforms can be performed on the in-band waveformand the out-of-band waveform on a per window or per array basis.Analysis of the transform coefficients (e.g. average, mean square, andthe like) can then be used to characterize noise or detect windows whennoise is present or absent. When noise is detected, instead ofattenuating the signal in the time domain in 526, the transformcoefficients for the current window can be attenuated. Then, an inversetransform can be performed on the attenuated frequency-domain signal toobtain a noise reduced time-domain signal.

In FIG. 2A (step 510), FIGS. 8B and 8D (step 580), or any otherembodiment where cross-correlation between two time-series waveforms isrequired, Fourier transform algorithms may be used for efficientcomputation of cross-correlation.

Furthermore, as explained above with reference to FIGS. 7A-7D, machinelearning algorithms may be used to classify noise patterns in step 570.These patterns may be defined by frequency domain and/or time-frequencydomain features, which will require frequency domain or time-frequencydomain processing of time-series waveforms.

Generalization Beyond Ultrasound

Although the preceding example systems and methods for image noisereduction have been illustrated within the context of ultrasoundimaging, it will be understood that the embodiments disclosed herein maybe adapted to a wide variety of imaging devices, systems and methods.

Another example of an imaging system that may be adapted for noisereduction according to the aforementioned embodiments is a magneticresonance imaging system. Referring now to FIG. 9 , an alternativeexample system is illustrated in which the signals that undergo noisereduction are obtained from a magnetic resonance (MR) system. Theexample system includes a magnetic resonance scanner 50 that employs amain magnet 52 to produce a main magnetic field B0, which generates apolarization in a patient 60 or the examined subject. The example systemincludes gradient coils 54 for generating magnetic field gradients. Areception coil 58 detects the MR signals from patient 60. The receptioncoil 58 can also be used as a transmission coil. Alternatively, bodycoil 56 may be employed to radiate and/or detect radio frequency (RF)pulses. The RF pulses are generated by an RF unit 65, and the magneticfield gradients are generated by a gradient unit 70. The manner by whichMR signals are detected using the sequence of RF pulses and magneticfield gradients, and how MR images are reconstructed in general, areknown to those skilled in the art.

A reference receive circuit may comprise a coil that is in the same roomas the scanner, but is not located immediately adjacent to the imagedsample (such as a patient) from which the sought-after MRI signals areemitting. Electromagnetic noise that is traveling near the MRI machinewill be detected by both the imaging reception coil 58 and the referencereceive circuit. The coil of a reference receive circuit might beoriented and positioned such that it is likely to receive some of thesame noise as the imaging reception coil, but be distant enough from theimaged sample such that there is negligible imaging energy detected bythe reference receive circuit.

The reference receive circuit coil may be tuned to have the samebandwidth as the imaging reception coil 58, or it may have a differentbandwidth that is still able to collect noise signals in the environmentthat are correlated with noise that might be coupled into the imagingreception coil.

The reference receive circuit may further comprise a collection ofreceive coils, such as 3 coils whose alignments are orthogonal to eachother. This would allow the collection of electromagnetic noise in amanner that a weighted sum of the noise collected in each of the 3 coilsmight more closely match the noise collected in the imaging receptioncoil, thus taking into account the directionality of dominant sources ofelectromagnetic noise in the MRI environment.

It will be understood that the MR system can have additional units orcomponents that are not shown for clarity, such as, but not limited to,additional control or input devices, and additional sensing devices,such as devices for cardiac and/or respiratory gating. Furthermore, thevarious units can be realized other than in the depicted separation ofthe individual units. It is possible that the different components areassembled into units or that different units are combined with oneanother. Various units (depicted as functional units) can be designed ashardware, software or a combination of hardware and software.

In the example system shown in FIG. 9 , control and processing hardware100 obtains magnetic resonance images of patient 60 according to asuitable pulse sequence. Control and processing hardware 100 isinterfaced with magnetic resonance imaging scanner 50 for receivingacquired images and for controlling the acquisition of images. Controland processing hardware 100 receives image data from RF unit 65 andprocesses the imaging data according to the methods described below.

Control and processing hardware 100 may be programmed with a set ofinstructions which when executed in the processor causes the system toperform one or more methods described in the disclosure in order toreduce noise in signals obtained from the magnetic resonance imagingsystem. For example, as shown in FIG. 9 , control and processinghardware 100 may be programmed with instructions in the form of a set ofexecutable image processing modules, such as, but not limited to, apulse sequence generation module (not shown), an image acquisitionmodule (not shown), an image processing module 145, and a noisesuppression module 150. The pulse sequence generation, image acquisitionand image processing modules may be implemented using algorithms knownto those skilled in the art for pulse sequence generation, imageacquisition, and image reconstruction, respectively. RF data is receivedfrom RF coils 56 and/or 58, and optionally one or more reference receivecircuits. Data may be sampled and filtered to obtain an in-bandwaveform. In addition, either a reference waveform via a referencereceive circuit or a noise-detection-band waveform measured viafiltering of RF from coils 56 and/or 58 are collected. One or more noisesuppression methods described in FIGS. 2-8 may be employed for noisesuppression at 100. The pulse generation module establishes the sequenceof RF pulses and magnetic field gradients depending on the desiredimaging sequence, and the image acquisition module stores the MR signalsdetected by the coils 56 and/or 58 in raw data space. The imageprocessing module 145 processes the acquired optionally noise-suppressedRF data to perform image reconstruction of an MR image.

By being able to detect noise that is correlated to noise in thebandwidth of the imaging signal (either via a reference receive channelor a noise-detection band) the ability to estimate in-band noise andimprove the imaging signal by removing the estimated noise from theimaging signal is provided.

This would allow for either an improved SNR in a typical cage forshielding the MRI from environmental noise, or for operation of the MRIsystem in a more open/unshielded environment that is typically moresubject to noise.

EXAMPLES

The following examples are presented to enable those skilled in the artto understand and to practice embodiments of the present disclosure.They should not be considered as a limitation on the scope of thedisclosure, but merely as being illustrative and representative thereof.

Example 1: Noise Reduction of Unknown Noise Source Via AttenuationFactor (Example of Embodiment 5)

The present example involved the collection of ultrasound data using anintra-cardiac echo (ICE) system, in the presence of two noise sources.The transducer was configured to detect ultrasound energy at frequencyof 9 MHz. Two band pass filters were used in parallel to separate theradio-frequency (RF) signal into an imaging band of 7-13 MHz, and anoise-detection band of 15-25 MHz which is beyond the frequency range ofthe emitted ultrasound.

The first noise source was an electroanatomic mapping system (Carto® 3).The system has an electromagnetic tracking module and an impedance-basedtracking module, for which patches to measure impedance and estimatedevice position are attached to a patient. These patches can couple asignificant amount of noise into the imaging band of the ICE images. Inthe present experiments, a heart phantom was used in a saline bath. Theelectrodes from the impedance patches were submerged in the bath. Thesecond noise source was from powering on an ablation generator that wasconnected to the Carto®3 console. The noise generated from this secondnoise source was determined to likely be noise propagating from theablation generator, through the Carto®3 console and through the patchelectrodes into the saline bath when the generator was powered on.

FIG. 10A shows an ultrasound image collected in the absence of eithernoise source, while FIGS. 10B and 10C show the effect of the first andsecond noise sources, respectively, on the imaging quality.

Noise reduction of ultrasound waveforms detected by the ultrasoundtransducer of the ICE console was performed according to animplementation of the method illustrated in FIGS. 6A and 6D.

During a first baseline noise characterization stage of the presentexperiment, energy was detected within an imaging band in the absence ofreceiving imaging energy and in the absence of receiving noise energy,thereby obtaining an in-band baseline noise characterization array,denoted as Gi. The in-band baseline noise characterization array wasobtained from sampling an in-band waveform for 125 us at 200 MS/s. Asliding and overlapping window (window size=64 samples, 20% overlap) wasemployed, and in-band power measures were calculated for each window. Arepresentative maximum power (90 the percentile across all windows) wascalculated, and its value was assigned to threshold Ti.

During a second noise characterization stage of the present experiment,energy was detected within both the imaging band and thenoise-detection-band in the absence of the receiving of imaging energy(i.e. when the transducer was not being pulsed with a voltage, and was dhence not receiving ultrasound energy), thereby obtaining correlatedmeasurements of in-band noise and noise-detection-band noise, denoted asCi and Cn. The noise-detection band was configured as the frequency bandspanning approximately 15 to 25 MHz.

In the present example implementation involving an ICE system, 512waveforms (each 125 us in duration sampled at 200 MS/s) were obtained.Accordingly, 512 in-band and detection-band noise-characterizationwaveform pairs were employed to estimate noise characteristics. Asliding and overlapping window (window size=64, 20% overlap) wasemployed for both the imaging band and the noise-detection band toobtain windowed pairs of in-band and detection-band noisecharacterization arrays, denoted as Ci_(w) and Cn_(w), where thesubscript w is an integer denoting the window number. For each pair ofCi_(w) and Cn_(w), the power in the imaging band and the power in thenoise-detection-band were calculated. Only windows whose in-band powerwas greater than threshold Ti were selected for further noisecharacterization.

Statistical noise power measurements were calculated during noisecharacterization. A set of windows of the in-band noise characterizationarray whose power falls between the 96^(th) and 99^(th) percentiles ofin-band noise-characterization array power values was selected. Withinthis set, the window with the near-minimum noise-detection band power(20^(th) percentile within the set) was chosen as w_max, and powervalues P(Cn_(w_max)) and P(Ci_(w_max)) were calculated and denoted asPn_(max) and Pi_(max), respectively. Similarly, a set of windows of thein-band noise characterization array whose in-band power falls betweenthe 1^(st) and 5^(th) percentiles of in-band noise-characterizationarray power values was selected. Within this set, the window with thenear-minimum noise-detection band power (20^(th) percentile within theset) was chosen as w_min, and power values P(Cn_(w_min)) andP(Ci_(w_min)) were calculated and denoted as Pn_(min) and Pi_(min),respectively.

As shown in FIG. 6B, a slope m, and y-intercept c, of the line passingthrough the points (Pn_(max), Pi_(max)) and (Pn_(min), Pi_(min)). Arelaxation parameter β was set as unity. A function f was defined as:

${f({Pn})} = \left\{ \begin{matrix}{0,} & {{Pn} < {Pn}_{min}} \\{{\left( {\beta.m.{Pn}} \right) + c},} & {{Pn}_{min} \leq {Pn} \leq {Pn}_{max}} \\{{Pi}_{max},} & {{Pn} > {Pn}_{max}}\end{matrix} \right.$

wherein Pn_(w) is the power in the detection band. It will be understoodthat the present implementation is provided to illustrate an examplealgorithm for selecting a functional relationship between the relativepowers in the imaging and detection bands, and that other functionalforms may alternatively be employed.

The function f( ) was employed to perform noise reduction of in-bandimaging waveforms obtained during imaging (i.e. when the pulser wasperiodically emitting imaging energy, and ultrasound energy was beingreceived by the transducer receive circuit).

Waveforms in the imaging band and the noise-detection band were sampledto obtain pairs of in-band imaging arrays and detection-band imagingarrays, denoted as Qi_(θ) and Qn_(θ). θ is an index identifying a periodof acquisition corresponding to a scan line. The imaging arrays weretemporally segmented according to the windows, denoted as Qi_(θ,w) andQn_(θ,w), where the subscript w is an integer denoting the windownumber. For each windowed portion of the in-band imaging array and theout-of-band imaging array, powers P(Qi_(θ,w)) and P(Qn_(θ,w)), werecalculated, and denoted as Pi_(θ,w) and Pn_(θ,w) respectively.

For each window, out-of-band imaging power Pn_(θ,w) was used to estimatea noise energy, {circumflex over (P)}i_(N θ,w), within the in-bandimaging window Qi_(θ,w). {circumflex over (P)}i_(N θ,w) was calculatedas f(Pn_(θ,w)), where β was set to unity and parameters m, c, Pn_(max)and Pn_(min), were obtained in the noise characterization stage.{circumflex over (P)}i_(N θ,w) was clamped below Pi_(θ,w). Anattenuating factor was calculated as [Pi_(θ,w)−{circumflex over(P)}i_(N θ,w)]/{circumflex over (P)}i_(N θ,w). The attenuating factorwas clamped above 0.02. The attenuating factor was multiplied with thearray Qi_(θ,w) to obtain a noise reduced array Qi_(θ,w). Qi_(θ) for all512 scan lines were enveloped and processed by an image generator (step230).

The aforementioned algorithm was employed to reduce noise in the in-bandimaging waveforms when performing imaging in the presence of the firstnoise and second noise sources. FIGS. 11A and 11B show the imagesobtained in the presence of the first noise source, without (A) and with(B) the application of the present noise reduction algorithm. FIGS. 12Aand 12B show the images obtained in the presence of the second noisesource, without (A) and with (B) the application of the present noisereduction algorithm. A clear improvement in the signal-to-noise ratio(approximately 6 dB improvement) was observed in both cases. As afurther illustration, FIGS. 11C, 11D, and 11E show the images obtainedin the presence of the first noise source with the application of thepresent noise reduction method when the relaxation parameter β offunction f( ) was set as 0.5 (C), unity (D) and 1.5 (E).

Example 2: Noise Reduction of Periodic Noise Source with DelayCorrection (Embodiment 6)

In the present example, noise reduction of ultrasound waveforms detectedby the ultrasound transducer of the ICE console was performed accordingto an implementation of the method illustrated in FIGS. 8A and 8B. Thedata for this example was collected with an ICE console with thepresence of an electromagnetic tracker. The control unit of theelectromagnetic tracker was observed to generate a pseudo-periodic noisepattern in the ultrasound image data, as shown in FIG. 13A.

In the present example implementation involving an ICE system, onewaveform (125 us in length sampled at 200 MS/s) was obtained whencollecting data in raw/RF mode while the imaging transducer is notreceiving imaging energy.

Data was saved as a pair of arrays (i.e. sampled waveforms), the firstarray consisting of an in-band noise-characterization waveform (7-13MHz) sampled after having performed envelope detection, and acorresponding array consisting of an out-of-band noise-characterizationwaveform in the 15-25 MHz band sampled after having performed envelopedetection. These in-band and detection band noise-characterizationarrays are denoted as Ci and, Cn, respectively.

The correlated in-band noise and out-of-band noise was then used toperform noise reduction of the in-band imaging waveform during theacquisition of imaging data i.e. while the ultrasound transducer isreceiving ultrasound energy.

While imaging, a pair of arrays were obtained for several periods ofacquisition corresponding, each period corresponding to a scan line, θ.For each array pair, one array was obtained in the imaging band (7-13MHZ), and one array was obtained in the noise-detection-band (15-25MHz). Each array was 125 us in length sampled at 200 MS/s. The arrays,referred to as the in-band imaging and out-of-band imaging arrays, anddenoted as Qi_(θ) and Qn_(θ), respectively, were measured duringimaging, i.e. while the transducer was receiving imaging energy.

Each in-band imaging array was processed for noise reduction using thein-band noise characterization array to reduce noise from the in-bandimaging array.

The in-band and out-of-band imaging arrays Qi_(θ) and Qn_(θ) weresegmented into a plurality of time windows. In the present exampleimplementation, the window size was 800 samples (in this case 4 μs at arate of 200 MS/s), with a 75% overlap between adjacent windows. Theimaging arrays, temporally segmented according to the windows, aredenoted as Qi_(θ,w) and Qn_(θ,w), where the subscript w is an integerdenoting the window number.

Temporal alignment between Qn_(θ,w) and Cn was achieved on a per-windowbasis. The temporal alignment was achieved by calculating, thecross-correlation between the out-of-band noise characterization arrayCn and the out-of-band imaging array Qn_(θ,w), and selecting therelative time delay t corresponding to the maximum cross-correlation.Due to the correlation between the in-band noise and the out-of-bandnoise, this time delay t was also applied to align the in-band noisecharacterization array relative to the windowed in-band imaging array.The aligned in-band noise characterization array is then windowed(denoted by Ci_(w)).

A scaling factor was applied to the in-band noise characterization arrayCi_(w) before it was subtracted from the in-band imaging array Qi_(θ,w).A noise reduced in-band imaging array, Qi_(θ,w) was calculated accordingto Qi_(θ,w)=Qi_(θ,w)−αCi_(w), where α=0.25 was the scaling factor toaccount for windowing. Negative values were replaced with 0.

This process was repeated for each additional window, for each scanline. The aforementioned method was employed to reduce noise of the dataobtained when performing imaging in the presence of an Aurora™electromagnetic tracking system (Northern Digital Inc), which acted as anoise source. FIGS. 13A and 13B show the images in obtained in thepresence of the noise source, without (A) and with (B) the applicationof the present noise reduction method. A clear improvement in thesignal-to-noise ratio (approximately 5 dB) was observed when the noisereduction method was implemented.

The specific embodiments described above have been shown by way ofexample, and it should be understood that these embodiments may besusceptible to various modifications and alternative forms. It should befurther understood that the claims are not intended to be limited to theparticular forms disclosed, but rather to cover all modifications,equivalents, and alternatives falling within the spirit and scope ofthis disclosure.

Therefore what is claimed is:
 1. A method of denoising imaging signalsdetected in the presence of broadband noise, the method comprising:detecting energy waves with an imaging transducer receive circuit,thereby obtaining an imaging waveform, and filtering the imagingwaveform to generate an in-band imaging waveform residing within animaging band and an out-of-band noise-detection imaging waveformresiding within a noise-detection band that lies, at least in part,beyond the imaging band; detecting an in-band imaging envelope of thein-band imaging waveform; detecting an out-of-band envelope of theout-of-band noise-detection imaging waveform; applying a scaling factorto the out-of-band envelope, thereby obtaining a modified out-of-bandenvelope; and combining the modified out-of-band envelope and thein-band imaging envelope to obtain a noise-corrected in-band envelope;wherein the scaling factor is selected to reduce a contribution ofin-band noise in the noise-corrected in-band envelope.
 2. The methodaccording to claim 1 further comprising adjusting a relative delaybetween the modified out-of-band envelope and the in-band imagingenvelope prior to combining the modified out-of-band envelope and thein-band imaging envelope.
 3. The method according to claim 2 wherein therelative delay is determined by calculating a cross-correlation betweenthe in-band imaging envelope and the modified out-of-band envelope. 4.The method according to claim 1 wherein the imaging transducer receivecircuit comprises an ultrasound transducer.
 5. The method according toclaim 1 wherein the imaging transducer receive circuit comprises a coilfor detecting a magnetic field.
 6. A method of denoising imaging signalsdetected in the presence of noise, the method comprising: detectingenergy waves with an imaging transducer receive circuit, therebyobtaining an imaging waveform, and filtering the imaging waveform togenerate an in-band imaging waveform residing within an imaging band andan out-of-band noise-detection imaging waveform residing within anoise-detection band that lies, at least in part, beyond the imagingband; applying a frequency shift and an amplitude scaling factor to theout-of-band noise-detection imaging waveform, thereby obtaining amodified waveform, such that the modified waveform includes frequencycomponents residing within the imaging band; and combining the modifiedwaveform and the in-band imaging waveform to obtain a noise-correctedin-band imaging waveform; wherein the amplitude scaling factor isselected to reduce a contribution of in-band noise in thenoise-corrected in-band imaging waveform.
 7. The method according toclaim 6 further comprising adjusting a relative delay between themodified waveform and the in-band imaging waveform prior to combiningthe modified waveform and the in-band imaging waveform.
 8. A method ofdenoising imaging signals detected in the presence of noise, the methodcomprising: detecting energy waves with an imaging transducer receivecircuit, thereby obtaining an imaging waveform, and filtering theimaging waveform to generate an in-band imaging waveform residing withinan imaging band; and detecting noise with a reference receive circuitconfigured to avoid transduction of imaging energy while detecting noisereceived by the imaging transducer receive circuit, thereby obtaining areference noise-detection waveform; detecting an in-band imagingenvelope of the in-band imaging waveform; detecting a reference envelopeof the reference noise-detection waveform; applying a scaling factor tothe reference envelope, thereby obtaining a modified reference envelope;and combining the modified reference envelope and the in-band imagingenvelope to obtain a noise-corrected in-band envelope; wherein thescaling factor is selected to reduce a contribution of in-band noise inthe noise-corrected in-band envelope.
 9. The method according to claim 8further comprising adjusting a relative delay between the modifiedreference envelope and the in-band imaging envelope prior to combiningthe modified reference envelope and the in-band imaging envelope. 10.The method according to claim 9 wherein the relative delay is determinedby calculating a cross-correlation between the in-band imaging envelopeand the modified reference envelope.